{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4e525d6199d8897a",
   "metadata": {},
   "source": [
    "## 逻辑回归-芯片测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-18T13:47:12.838597Z",
     "start_time": "2025-06-18T13:47:12.831539Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 导包\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e196998554d35901",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-18T13:47:13.078157Z",
     "start_time": "2025-06-18T13:47:13.041892Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>param_1</th>\n",
       "      <th>param_2</th>\n",
       "      <th>validity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.051267</td>\n",
       "      <td>0.69956</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.092742</td>\n",
       "      <td>0.68494</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.213710</td>\n",
       "      <td>0.69225</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.375000</td>\n",
       "      <td>0.50219</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.513250</td>\n",
       "      <td>0.46564</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    param_1  param_2  validity\n",
       "0  0.051267  0.69956         1\n",
       "1 -0.092742  0.68494         1\n",
       "2 -0.213710  0.69225         1\n",
       "3 -0.375000  0.50219         1\n",
       "4 -0.513250  0.46564         1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "data = pd.read_csv(\"../data/microchips-tests.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b735ad73f49b470",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-18T13:47:13.341087Z",
     "start_time": "2025-06-18T13:47:13.328884Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      False\n",
      "1      False\n",
      "2      False\n",
      "3      False\n",
      "4      False\n",
      "       ...  \n",
      "113     True\n",
      "114     True\n",
      "115     True\n",
      "116     True\n",
      "117     True\n",
      "Name: validity, Length: 118, dtype: bool\n"
     ]
    }
   ],
   "source": [
    "# add label mask\n",
    "mask = data.loc[:, \"validity\"] == 1\n",
    "print( ~mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "73d7df9373607e43",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-18T13:47:14.072028Z",
     "start_time": "2025-06-18T13:47:13.572063Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘图 分别标识颜色\n",
    "fig2 = plt.figure()\n",
    "passed = plt.scatter(data.loc[:, \"param_1\"][mask], data.loc[:, \"param_2\"][mask])\n",
    "failed = plt.scatter(data.loc[:, \"param_1\"][~mask], data.loc[:, \"param_2\"][~mask])\n",
    "plt.title(\"param_1 vs param_2\")\n",
    "plt.xlabel(\"param_1\")\n",
    "plt.ylabel(\"param_2\")\n",
    "plt.legend((passed, failed), (\"Passed\", \"Failed\"))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b1062c1863a20abd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-18T13:47:14.344329Z",
     "start_time": "2025-06-18T13:47:14.311853Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           X1        X2      X1_2      X2_2     X1_X2\n",
      "0    0.051267  0.699560  0.002628  0.489384  0.035864\n",
      "1   -0.092742  0.684940  0.008601  0.469143 -0.063523\n",
      "2   -0.213710  0.692250  0.045672  0.479210 -0.147941\n",
      "3   -0.375000  0.502190  0.140625  0.252195 -0.188321\n",
      "4   -0.513250  0.465640  0.263426  0.216821 -0.238990\n",
      "..        ...       ...       ...       ...       ...\n",
      "113 -0.720620  0.538740  0.519293  0.290241 -0.388227\n",
      "114 -0.593890  0.494880  0.352705  0.244906 -0.293904\n",
      "115 -0.484450  0.999270  0.234692  0.998541 -0.484096\n",
      "116 -0.006336  0.999270  0.000040  0.998541 -0.006332\n",
      "117  0.632650 -0.030612  0.400246  0.000937 -0.019367\n",
      "\n",
      "[118 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "# 逻辑回归\n",
    "# 一阶 决策边界函数 a+b*X1+c*X2 = 0\n",
    "# 二阶 决策边界函数 a+b*X1+c*X2+d*X1^2+e*X2^2+fX1*X2 = 0\n",
    "# X数据就是测试数据\n",
    "X = data.drop(columns=[\"validity\"])\n",
    "# Y数据是通过情况\n",
    "y = data.loc[:, \"validity\"]\n",
    "X1 = data.loc[:, \"param_1\"]\n",
    "X2 = data.loc[:, \"param_2\"]\n",
    "\n",
    "# create new data\n",
    "X1_2 = X1 * X1\n",
    "X2_2 = X2 * X2\n",
    "X1_X2 = X1 * X2\n",
    "\n",
    "X_new ={\"X1\": X1, \"X2\": X2, \"X1_2\": X1_2, \"X2_2\": X2_2, \"X1_X2\": X1_X2}\n",
    "X_new = pd.DataFrame(X_new)\n",
    "print(X_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "94a8c311d949410a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-18T13:47:14.624813Z",
     "start_time": "2025-06-18T13:47:14.596230Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url();\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('penalty',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">penalty&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('dual',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">dual&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">tol&nbsp;</td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('C',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">C&nbsp;</td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">intercept_scaling&nbsp;</td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">class_weight&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('solver',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">solver&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_iter&nbsp;</td>\n",
       "            <td class=\"value\">100</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('multi_class',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">multi_class&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose&nbsp;</td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">warm_start&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('l1_ratio',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "LR2 = LogisticRegression()\n",
    "LR2.fit(X_new, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b7345ef0239abb4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-18T13:47:14.851954Z",
     "start_time": "2025-06-18T13:47:14.838444Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8135593220338984\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "y_predict = LR2.predict(X_new)\n",
    "accuracy = accuracy_score(y, y_predict)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cf1b5e8e066222a5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-18T13:47:15.619480Z",
     "start_time": "2025-06-18T13:47:14.995672Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\python\\machine-learn\\.venv\\Lib\\site-packages\\pandas\\core\\arraylike.py:399: RuntimeWarning: invalid value encountered in sqrt\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X1_new = X1.sort_values()\n",
    "theta0 = LR2.intercept_\n",
    "theta1, theta2, theta3, theta4, theta5 = LR2.coef_[0][0], LR2.coef_[0][1], LR2.coef_[0][2], LR2.coef_[0][3], LR2.coef_[0][4]\n",
    "a = theta4\n",
    "b = theta5*X1_new + theta2\n",
    "c = theta0 + theta1*X1_new + theta3*X1_new*X1_new\n",
    "X2_new_boundary = (-b + np.sqrt(b*b - 4*a*c))/(2*a)\n",
    "\n",
    "fig3 = plt.figure()\n",
    "passed = plt.scatter(data.loc[:, \"param_1\"][mask], data.loc[:, \"param_2\"][mask])\n",
    "failed = plt.scatter(data.loc[:, \"param_1\"][~mask], data.loc[:, \"param_2\"][~mask])\n",
    "plt.plot(X1_new, X2_new_boundary)\n",
    "plt.title(\"param_1 vs param_2\")\n",
    "plt.xlabel(\"param_1\")\n",
    "plt.ylabel(\"param_2\")\n",
    "plt.legend((passed, failed), (\"Passed\", \"Failed\"))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d1277428925360cb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-18T13:47:15.752911Z",
     "start_time": "2025-06-18T13:47:15.738970Z"
    }
   },
   "outputs": [],
   "source": [
    "# define f(x) 已知X1 求解\n",
    "# a + b*X1 + c*X2 + d*X1^2 + e*X2^2 + f*X1*X2  => e*X2^2 + (f*X1 + c)*x2 + (a + b*X1 + d*X1^2) = 0\n",
    "def f(x):\n",
    "    a = theta4\n",
    "    b = theta5*x + theta2\n",
    "    c = theta0 + theta1*x + theta3*x*x\n",
    "    X2_new_boundary1 = (-b + np.sqrt(b*b - 4*a*c))/(2*a)\n",
    "    X2_new_boundary2 = (-b - np.sqrt(b*b - 4*a*c))/(2*a)\n",
    "    return X2_new_boundary1, X2_new_boundary2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2b8beefca41dede7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-18T13:47:15.989420Z",
     "start_time": "2025-06-18T13:47:15.910371Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([0.12118557]), array([0.046756]), array([0.02694692]), array([0.00869419]), array([-0.00832964]), array([-0.00832964]), array([-0.11719503]), array([-0.16040596]), array([-0.18016739]), array([-0.18965406]), array([-0.21670972]), array([-0.22529994]), array([-0.23369318]), array([-0.24992385]), array([-0.28761169]), array([-0.30846342]), array([-0.32171357]), array([-0.32815515]), array([-0.3344791]), array([-0.34068868]), array([-0.35277682]), array([-0.35866103]), array([-0.37012269]), array([-0.42185219]), array([-0.42655662]), array([-0.43118989]), array([-0.43573725]), array([-0.44020804]), array([-0.45317261]), array([-0.4733505]), array([-0.4733505]), array([-0.48464004]), array([-0.48827191]), array([-0.49183935]), array([-0.49183935]), array([-0.51192772]), array([-0.51192772]), array([-0.51813664]), array([-0.52410902]), array([-0.52985346]), array([-0.52985346]), array([-0.53263691]), array([-0.54064711]), array([-0.54570741]), array([-0.55054607]), array([-0.55054607]), array([-0.55516755]), array([-0.5616945]), array([-0.56774088]), array([-0.56774088]), array([-0.58000745]), array([-0.58000745]), array([-0.58587238]), array([-0.59312103]), array([-0.59414883]), array([-0.59512487]), array([-0.59851243]), array([-0.60050853]), array([-0.60278923]), array([-0.60308716]), array([-0.60352336]), array([-0.60377527]), array([-0.60353814]), array([-0.60280452]), array([-0.60103842]), array([-0.60103842]), array([-0.60045365]), array([-0.59132679]), array([-0.59132679]), array([-0.59007409]), array([-0.58875706]), array([-0.58283085]), array([-0.58283085]), array([-0.57387691]), array([-0.56071089]), array([-0.55569363]), array([-0.53216302]), array([-0.52177716]), array([-0.51810801]), array([-0.51810801]), array([-0.50644082]), array([-0.48921784]), array([-0.47983171]), array([-0.46987923]), array([-0.45379411]), array([-0.42344643]), array([-0.40271714]), array([-0.38764345]), array([-0.37141056]), array([-0.35384368]), array([-0.34517051]), array([-0.33471388]), array([-0.33471388]), array([-0.32445203]), array([-0.26408872]), array([-0.26408872]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan])] [array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([0.41131186]), array([0.47882404]), array([0.49632333]), array([0.51227026]), array([0.52698829]), array([0.52698829]), array([0.61740733]), array([0.65139507]), array([0.66654491]), array([0.67372579]), array([0.69386406]), array([0.70014848]), array([0.70623593]), array([0.71785501]), array([0.74400987]), array([0.75794422]), array([0.76658278]), array([0.77071856]), array([0.77473672]), array([0.77864049]), array([0.78611705]), array([0.78969546]), array([0.79654553]), array([0.82521708]), array([0.82761571]), array([0.82993919]), array([0.83218075]), array([0.83434574]), array([0.84039293]), array([0.84904184]), array([0.84904184]), array([0.853414]), array([0.85474008]), array([0.85600172]), array([0.85600172]), array([0.86225532]), array([0.86225532]), array([0.86385265]), array([0.86521343]), array([0.86634228]), array([0.86634228]), array([0.86681994]), array([0.86791276]), array([0.86836146]), array([0.86858934]), array([0.86858934]), array([0.86859882]), array([0.86820799]), array([0.86733658]), array([0.86733658]), array([0.86346154]), array([0.86346154]), array([0.86010273]), array([0.85351581]), array([0.85223741]), array([0.85090766]), array([0.84507164]), array([0.84014996]), array([0.83090168]), array([0.82889381]), array([0.82471842]), array([0.81805295]), array([0.81089443]), array([0.80324342]), array([0.79225413]), array([0.79225413]), array([0.78936357]), array([0.75717876]), array([0.75717876]), array([0.75362027]), array([0.74999744]), array([0.73484805]), array([0.73484805]), array([0.71436113]), array([0.68736034]), array([0.67773149]), array([0.63575452]), array([0.61845128]), array([0.61247633]), array([0.61247633]), array([0.59389175]), array([0.56744159]), array([0.55344386]), array([0.53887979]), array([0.51587729]), array([0.47400064]), array([0.44635396]), array([0.42666868]), array([0.4058242]), array([0.38364573]), array([0.37283089]), array([0.35990434]), array([0.35990434]), array([0.34733269]), array([0.2754404]), array([0.2754404]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan])]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rocpenghua\\AppData\\Local\\Temp\\ipykernel_6340\\1894809486.py:7: RuntimeWarning: invalid value encountered in sqrt\n",
      "  X2_new_boundary1 = (-b + np.sqrt(b*b - 4*a*c))/(2*a)\n",
      "C:\\Users\\rocpenghua\\AppData\\Local\\Temp\\ipykernel_6340\\1894809486.py:8: RuntimeWarning: invalid value encountered in sqrt\n",
      "  X2_new_boundary2 = (-b - np.sqrt(b*b - 4*a*c))/(2*a)\n"
     ]
    }
   ],
   "source": [
    "# 获取边界\n",
    "X2_new_boundary1 = []\n",
    "X2_new_boundary2 = []\n",
    "for i in X1_new:\n",
    "    X2_new_boundary1.append(f(i)[0])\n",
    "    X2_new_boundary2.append(f(i)[1])\n",
    "print(X2_new_boundary1, X2_new_boundary2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "46786eeb359db028",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-18T13:47:16.450460Z",
     "start_time": "2025-06-18T13:47:16.086846Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 边界绘图不完整，因为数据量不够\n",
    "fig4 = plt.figure()\n",
    "passed = plt.scatter(data.loc[:, \"param_1\"][mask], data.loc[:, \"param_2\"][mask])\n",
    "failed = plt.scatter(data.loc[:, \"param_1\"][~mask], data.loc[:, \"param_2\"][~mask])\n",
    "plt.plot(X1_new, X2_new_boundary1)\n",
    "plt.plot(X1_new, X2_new_boundary2)\n",
    "plt.title(\"param_1 vs param_2\")\n",
    "plt.xlabel(\"param_1\")\n",
    "plt.ylabel(\"param_2\")\n",
    "plt.legend((passed, failed), (\"Passed\", \"Failed\"))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7a74818ab6470fb4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-18T13:47:21.533249Z",
     "start_time": "2025-06-18T13:47:16.514303Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rocpenghua\\AppData\\Local\\Temp\\ipykernel_6340\\1894809486.py:7: RuntimeWarning: invalid value encountered in sqrt\n",
      "  X2_new_boundary1 = (-b + np.sqrt(b*b - 4*a*c))/(2*a)\n",
      "C:\\Users\\rocpenghua\\AppData\\Local\\Temp\\ipykernel_6340\\1894809486.py:8: RuntimeWarning: invalid value encountered in sqrt\n",
      "  X2_new_boundary2 = (-b - np.sqrt(b*b - 4*a*c))/(2*a)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan])]\n"
     ]
    }
   ],
   "source": [
    "# 构造大量数据，构建新的边界数据（函数不变）\n",
    "X1_range = [-0.9 + x/10000 for x in range(0, 19000)]\n",
    "X1_range = np.array(X1_range)\n",
    "X2_new_boundary1 = []\n",
    "X2_new_boundary2 = []\n",
    "for i in X1_range:\n",
    "    X2_new_boundary1.append(f(i)[0])\n",
    "    X2_new_boundary2.append(f(i)[1])\n",
    "print(X2_new_boundary1, X2_new_boundary2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "20ed0354f293878e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-18T13:47:22.644958Z",
     "start_time": "2025-06-18T13:47:22.009188Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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V0IQS48F0g9zoxUJSrlw5FSidW6Q8gGS9STC3xCiJ682VFiUzn4fs3r8Zs0QIIUaD8WAehQgjZ5YHIFlDsUQIIUaOByOEOB3GLBFCCCGE2IFiiRBCCCHEDhRLhBBCCCF2oFgihBBCCLEDA7yJ+RuPMmPI2PAcEkLcjKEsS1KwqlOnToiMjFRdiaWKZ1ZIBVAphR4QEKCqj86ZM+eedaS3jXQ/lvoL0qRvy5YtTjoC4vJaNJNrAHM7At/308YyLfOJ4wXN8T+BPYu0sUw7Ap5DQogOMJRYunHjhqogam3clxVShKpDhw6pDQGlDLs0+ZNS7FakF83gwYNVd2QpzS7bb9u2LWJjY514JMTpyM10Qa97O7THRWvzebN1HM4SNDyHhBCdYCixJB2Lx44dm+3y5tKzRip2StM/KZ0+cOBAPPnkk/jvf/+buo6Ubu/fvz/69u2LatWqqfdI2fVZs2Zlut2EhARV9TPtQHSEWDWkunGG7Sfvzosa5jjrhyfjLEHDc0iI4ZgzZw7Cw8NTp9977z3UqVMnT9s8ceKE8iSJwcOdGEos5ZSNGzeidevWNvPEaiTzhTt37qiOxWnXkU7KMm1dJyOkF46UR7cO0mGZ6AiJb0l/87bBAsSd1dYjuceZgobnkBDnu7kzoU+fPkqgpB+OHDli933dunXD33//DTNi6gDvmJgYFCtWzGaeTIsl6NatW7hy5Ypq+pfROtLRODOkEaC47qzI9iiYdIQEAjtyPZJ3QZPTStM8h4TY6QMYqTVUdmIfQGmCO3v2bJt5RYoUsfueoKAgNZgRU1uWnIUEi0vDvbQD0RGSMeXI9XSENNTcePQSftp5Vo1l2m04U9CY+BwSYoS4PbnPRURE2AyffPIJatasiZCQEGUgGDBgAOLj4zN1w2XEF198ocJiJKGqSpUq+Oyzz2yWS4JV3bp11fIGDRpgx44d0AOmtizJyT1/3vZCLdMibkT9+vj4qCGjdeS9xKBIark8eckFJUMXkZe2XNYzEFF7ozF66X5EX7udOq94WCBGdaqGdjWKu36HnCloTHoOCXGcm9tLc3NLQ2UXldLw9vbGlClTVCzwsWPHlFgaOnToPYInM+bNm4eRI0fif//7nxJEIoQkZljEV+/evZXw6tixIx555BF8/fXXKknrjTfegB4wtWWpSZMmWLVqlc28lStXqvmCv78/6tevb7NOSkqKmrauQwyIXDjERK3wSrfw7nS7jwxVq0eE0itfb7cRSkLMtdtqvix3OVZBc893nFbQlMidoDHhOSTESHF7y5YtQ758+VKHp556SmWUS3a5lNpp2bKlSrhasGBBtrcpWeeScPX4448rwSXjN998E59//rla/s0336h78MyZM1G9enUlnN5++23oAUOJJVGdEhFvjYoX1SmvT506lRpL1KtXr9T1X375ZaV+RflKDJKoXzmxcnKsSOzRjBkzMHfuXBw4cACvvPKKKlEg2XHEwIgv/+kvgdB0Fhe5uct8J/r6HY242sSiZCeMWi13uUvO2YLGROeQkBzj5rg9a8kd6zBlyhT89ttvaNWqFUqUKIH8+fPjueeew6VLl3Dz5s0styf31aNHj6Jfv342IkwEl8wX5B5cq1Yt5YKzohfDhaHccH/99Zc6gVasQdZivhNfaXR0dKpwEkS5/vzzz0ocia+1ZMmSyl8qGXFpo/cvXLigTIMSEC5pjlFRUfcEfRMDIjdTMVEbvPrzluOX77EopUUkkiyX9ZpUKOTSfUsVNBkGoH6Ud0FjknNIiNHi9sQ1JoWc06bwd+zYURkUPvjgAxQsWBDr1q1T4kcyy6Xkjj2ssU1inJDiz2mRcBi9Yyix1KJFC1gsmT89Z1SdW96TVYCY1F+SgZgQuanmNBNLZ8Rev+3Q9RyOswWNUc8h27SQvKCzuL1t27YpF5m40SR2SciJC04MENJ9Q7w9zz77bIbrSOD3V199hdu3b6dalzZt2gQ9YCixRIgnUjR/oEPXcwpGFTQmS/cmJsLq5pasN+XWtrg1bq9ixYpITEzEp59+qtqOrV+/XhVxzgmjR4/G66+/ruoTSmkCKfAsHiMp4yOeoh49euBf//qXCvqWsBqxZk2cOBF6wFAxS4R4Ig3LFVRZb3bCqNVyWY/oALZpIY5CR3F7tWvXVh0vxo8fjxo1aqjMNinQnBOk3ZiEwkj9JilB0Lx5c+URkpAZQWKYli5dij179qhsORFO8nl6wMtiz69FsoUUpRSlfO3aNdZc8kRc4G6xZsMh4+dLTOtZzz3lA/SOq11h8nnSFy/TLKa7rpNBe+iS8wDEnSSJSCIG0gYt5xi6dJ12HrJ7/6YbjhADuFtECIkgSl9nKcKddZb0jjtcYc6sak48F7q53Q7FEiF5dbekD760ulscbCYXQfRItQiV9SbB3BKjJK43H+/MHHQejIvPTSps00KIKaFYIubBlaZqN1XXFWHk8vIARsOdlY/ZpoUQU0KxRMyBq10udLfoF3eeG52lexN9wNBg43//zIYjxscd2Ud0t+gXd54btmkxrjXy+J/AnkXaWKYdgLXYohRtJO7DWmHcz88v19ugZYkYG3e5XOhu0S/uPjfOrmpODGOV9vX1VZWtpUuE3KitxRyJ6yxKIpRiY2MRHh6ep0rhFEvE2LjL5UJ3i37Rw7lhmxZj4OREAC8vLxQvXlylrZ88eTLv+0tyhQiliIgI5AWKJWJs3OVy0Vl1XaLDc8N0b33jIqu0v78/KlWqRFecmxCLniN6z1EsEWPjTpcL3S36heeG6MgqLe63PBWlJG6HYokYG3e7XOhu0S88N8QeTNIgOYBiiRgbPbhc6G7RLzw3RK+JAMRQUCwR40OXi3tJTgKS7wApiUDy3cH62ssL8PIBvH014SJjL2/Axw/wCxH/hLv3nngq7rZKE0NBsUTMAV0ueUcKt928BFyPAW7EAvEX7o5jgRsXgFtXgYTrQELc3fHdITkh95/pFwz4h9wd8mnjwDAguJA2hBQGggvffV0EyB8B5C8O+PDSRUxglSaGgVccYh7ocsmaxNvApSPApcPA1VPacOXkP6+TbuX9M8Ry5O2nWY+ElCQt88giQ0q6/bmpDSLGcrL9fBFAWAkgtAQQVhIIKwUUqgAULA+El6GYItmDVmmSTbwsrMOeZ+Li4hAWFoZr164hNDTU3btDiOYau3AQiNmjjS/+rY2vnLhXsNjgpVlx8hXVLDlqXBTIVwQIKgAEhN4d8v8ziDXIx18TRyKS7LnW5HIjwkncdiKS7sQDd27cHeKBhHjg9lXgxkXNyiWD9bWMr0drLj57iKuvQDlNPBWqCBSuBBSrARStqu0rIe7sK0kMef/m4xchZrjQixA6t+PusBM4vxdIup3x+uLmKnyfJijCS9sOYqHx9XfevkoMk1h9ZPAP1txsOSElRXMNXjsLxJ25Oz4LXD0JXDoGXD6qHbdYzmSw/XCgYDlNOKmhOlC8tmaZkv0ingut0iQLaFlyALQsEZeSlACc3Q6c2gCc3Aic3qzFEaXHPz9QvJZmUSlSRRNIRSprT85mFQcipkQ8iWgSd+PFI5qQPL9PE1kZId9HiQZAiXpAyQZAZD0gkP/HqdDqQkxMdu/fFEsOgGKJOF0ARO8Ejq4Cjq4Fzmy9N6hahFFkHc1SElkXKF5Hi9/RebZZcooFW45fRuz12yiaPxANyxWEj7eThJwEqotoUsNeIGYvELtfi6WywUsTlaWbAGWbAmUeBEKLwyNxYt80QvQAxZILoVgiDkcy0Y78dlcgrQFuXrRdLvFEcjOXp3wZR9Q03NN+1N5ojF66H9HX/nEXFg8LxKhO1dCuhovEyZ2bQPQu4Ow24OxfwJltwLVT965XsAJQ9kGgTFNNQElwuaf2TbNmiuWxbxoheoBiyYVQLBGHINloB5YBB5cBpzbaBmKL5ah8c6BCS6Bccy142cCuNBFKr3y9PbPbMKb1rOc6wZSRBUqsdyfWAyfXAdG77xUM4tas2FobRLD6BsB0rrfJNey0A7lbg2jQHsOJdELSQrHkQiiWSK6R7LQ9i4ADSzVXW1oiagGVHgEqtAJKNfwnFd/grjHZftPxq20sSmmRT4oIC8S6d1o6zyWXE6S+1KlNmnA6IeJpl62QlVpRZZtpwknOlwSRG53jfwJzO2a9Xu9lDIwmhobZcITolVtXgH0/Aru/0yxIaesHiUutaietwKZkp5nQNSZCLDOhJMjTmyyX9ZpUKAS3ExQOVG6nDcLNy8CxtcCRVZqrND4GOPyLNqyAlmlXpSNQtaP22ogWQPZNI8QGiiVCXOXWkJvrjq+Av6O0OkMKL829Vv1xoPKjWj0jnbnGYq7dVvMd5RoTi5Uj13M5wQWBGo9rgxjmJWBcRJMMkjUmweMy/P4RUKCsJpxkKNVI9wH3qbBvGiE2UCwR4uz4FxFI2+ZoMUlWilYHancDaj6lxX64GXGNiUUpI5+8zBPbiCx/pFpEnl1j4tpz5HpuRaxGETW0oekgzeokYlhizyQ4X9ysG/+nDfkjNYEl51yyFvVscWLfNEJsoFgixNGItUFiXLb8nxaLZK04HRgO1OmhDZK9pod0eje4xuR4xLUnFiuLnZglWc9w9YTE6mQ9x1KVXKxNIpz+/gW4fu4f4SQ1r0Q0VX9M68WntxpG7JtGiA0US4Q48mYr4mjDp1oaupWS9wMNntdujH5Bukynd6VrTISfHI+49jK5DavlLg3udkY9IWmtUq2LNkgh0cMrgT0LNcuTtJ9Z84E2pEVPNYzYN42QVJgN5wCYDefhSK2enfOAjVOBK8e1eT4BQO3uwP0vaFW0dZ5Ov/HoJTwzY1OW633bv7HDgq51UWfJHfWEbscBq8cCWz7PfJ2nvgSqd4EuYAVvYmKYDUeIK0TSX7OA9ZOBGxe0edJs9v7+QMMXcxSs7cqYIb24xkQQyfG42uV4jxAQy4m9bz5qmJad6CiBIBang0vtr/P988DlfwF1e2rNjN0J+6YRQrFESI5JvKUFbK/77z+p05Lm3+Q1oO6zueps7+50ene5xmR7bi0PIBaTTAsvChat15ys5yjBkOVniohLAlaNBtaO09y3Ir6lb52ZoQWL6BiKJUKyS3ISsONLYO14rbaOVSQ99DZQ+5k8FY3UQzq9WHrE1ZfeNRbhDteYmesJZXdb0tvv8jGtHpcM0uC30UuaeDJbxXD2oCM6h2KJkKyQsD4Jzl05QutgL4SVApoNAeo8C/j65/kj9JJOrwvXmNnrCWV3W52maFZKyarc+z1wbjuw+CXg138D9fsCDfu730XnzJgxKVsg89mDjugAg1RI+4epU6eibNmyCAwMRKNGjbBly5ZM123RogW8vLzuGTp06JC6Tp8+fe5Z3q7d3Uq9hEhn+q+6At88pQkliUmSp93XtgEN+jpEKKWNGcpMksj84i5Kp7e6xrrUKaHGphVKaesJ2fvmQ0s4tp5QTj6zRD3gsenAm/uBlv/WajVJfNwfHwP/rQEse1OzPhmVLGPGJBNgmLYeIW7EUGLpu+++w+DBgzFq1Chs374dtWvXRtu2bREbG5vh+j/88AOio6NTh71798LHxwdPPfWUzXoijtKu9+2337roiIhuuX0NWD4U+LyZ1trCxx944DXg9R1A45cd7gaxxgwhg1uo29LpPQFrPSF737yj6wnl5jMlWUDcvYN2A0/NAUrUB5ITtASDT+sDC3oD53bAcOQkZowQN2IosTRp0iT0798fffv2RbVq1TB9+nQEBwdj1qxZGa5fsGBBREREpA4rV65U66cXSwEBATbrFShQwO5+JCQkqHTDtAMxkctNXB7/a6ildkvDVKmT8+oWoM1YzbLk5JghiRFKi0w7s2yAx2OtJxSa7vsV64+zXEC5/UyJi5OYpRdWAX1+Bio+ov1G9/8I/F8LYG5n4PgfMAzuiBkTK5U0CpYG1jKm1YqYKWbpzp072LZtG4YPH546z9vbG61bt8bGjWmakdph5syZ6N69O0JCbLOV1q5di6JFiyqR1LJlS4wdOxaFCmWeoTNu3DiMHj06D0dDdMmlo8DPQ4Bja7TpghWADv8BKjzssl3wuJghvSDiRMoDuDIbKy+fKa1SyjbVBnEVb5hy9+b/uzaUaQo8PFxbrmdcHTPGQHJi9qKU586dQ4kSJbBhwwY0adIkdf7QoUPx+++/Y/PmzXbfL7FNEuMk6zVs2DB1/vz585W1qVy5cjh69Cjeffdd5MuXTwkwcdllZlmSwYpYlkqVKsWilEYlJQXY+gWwciSQdEsrKCnB2w++AfgZoD8ZIYL0Hlw/Bdg+959GzWWbAS1END0IXSJWnck1su5BN2hP3oWrq4uPEkPAopQZWJVq1qxpI5QEsTRZkeW1atVChQoVlLWpVatWGW5L3HYyEBNw5STw06vAiT//ubl0+gQoVMHde0ZIzpAyFh0mAk3fBNZNArZ/qf2u5/yp/a4lQLx0Y+gKV/Wgc0fxUWIqDBOzVLhwYWXpOX/e1nct0xJnZI8bN24oC1K/fv2y/Jzy5curzzpy5Eie95noOMZADKrb5gLTHtBuKH7BwKMTgV5LMhdKjHUgRiCshOY+lmSEBv0Abz/tNz6rLfBtD+DCIXhczBgDyUkeMYxlyd/fH/Xr18eqVavQtWtXNS8lJUVNDxw40O57Fy5cqNxmPXv2zPJzzpw5g0uXLqF4cQbT6gJnxBhIptvSN4B9i7Xp0k2ALlPtW5NMHusg7VbMFidlxmPKEWElgY6TNEuTlBrY8TVw6Gfg7xVA3ec091x6gWLWmDF3BJITU2GYmCVr6YDevXvj888/V+60yZMnY8GCBTh48CCKFSuGXr16qbgmCcBOS7NmzdR8sS6lJT4+XgVqP/HEE8o6JTFLEgN1/fp17NmzJ9uuNjbSdRLOiDE4uw1Y9Dxw5QTg7Qu0Ggk0GWj/omzgWIfsCAbdNLR1IGY8pjyf+3wX4LN6jCaYBLGmNh6gxeYFmvy6JZbguR2zXq/3MvbB8zDizBiz1K1bN1y4cAEjR45ETEwM6tSpg6ioKCWUhFOnTqkMubQcOnQI69atw6+//nrP9sStt3v3bsydOxdXr15FZGQk2rRpg/fff58xSe7G0TEG8kywaZoWxJ2SqMV3PDk7635bBo51yI5gkHWkH1z6o5OGujLfiCULzHhMjjv3/0E7qRcm/wdntgB/TtQCwlu/B9TuISnGMCXWQqBZBZI7svgoMRWGsizpFdNZlvTQ0NKRT4LS+HbJa8Cehdp01c5A50+BoHDX7ocOBIPVpiSCQUoUNB2/OtMGvl53azyte6dljtxX7nR/yWc745iMRHbOfbvqEcDBZcBv7wGX7sZnSqHL9h+bt2FvqoUYGQeS69hCTJyHKS1LxAXoJTbHUTEG184A858Fondqbre2H2od3KVOjSv3w4WIYBCrgh1bmFqeP9AvU1FhXVeWi/CRtidGcH/Jvjr6mIxEds+9CGWfqp2ASm214qvSHFpc1F+00vodthoF5HdgPzw9BZJneH37iEKJ2MWkNleSpyev9Fkj1oaWstxIxepObtSqGotQCi4EPPej1rU9u0LJUfvhYrIrGDYevZSt7YmFKCcWjfSfbXV/yXJnk919ze56RiMnYlEhvQ3FLSe9DkUkCTvnaS1UNk4FkpNyLNbkd/XTzrNqLNO6QgTRoL2aJfiJmdpYajhRKJEsoGWJ6DM2J68xBtKyZPHLWnG+YjWB7vOAAmVcvx9uIPtCIHs3MnGlOdSi4UT3V3b2NSfrGY1ci0WxInX9DGjwPLD8beDcduCXd4Hd32l1xyLr6t6qmG3k+qUjlzkxBrQsEX3WIclLg9MN/9My3kQoiauh3y+5E0p53Q83kV0h0KR8YXUzy0y6yHxZLjFHDrdoOAnZV0cdkxHJs1iUeCXpOycCKTAMiN4FzGgJRL0LJMTr2qroUbDmm8uhWCL6jc3JabE6aVsiF/Vf/6VNN3oZeGou4B/i2v1wM9kVDI0rFFJP/dZ56dcRZHl2LEF6cX/JvjrqmIyIQ8SiZMTV7wO8uhWo8YTWqHfTVOCzxsChqBxbFQVZrjuXnFGRcAhpESOJJ9/308Yy7cowCQ+EYonoOzYnuzEGElux+CXtoi488r5jLT4GinXIiWAQ94hkRxULtS2VIdM5SbHXk/vLekyS9ZYWmTZ72QCHikVxzT05C3h2kVZq49pp4NtuwKJ+wM3LurMqegR6iiv1MBizRPQfm5NVjEFyovaEtf8nLeOt63Sg1lOu3w8dYRUM6WNIIjKNIcns1pozi4a4XTL59ajPdpX7S45P4qM8sYJ3zs99FlR6BBiwCVj7EbDxf8DeRVr7lE5TgMrtdGNVND16iyv1MFhnyQGYps6SEeuQJCUAC/sAh5YDPv7aPlZu7+690g1Z1TzKVk2ebN5crdvK5NdjequO3nBKvasz24AfXwYu/q1N13kWWyq/jafn7s/yrd/2b2zscg3urj9n0JpvZrl/Uyw5ANOIpUzrLJXQZx0SEUpSQ+nISsA3EOg2D6jU+p7VPL5HmAsLOBomI4rkHinyunqsVloAFlhCS+KNW89j6fUqdq2Khi4Eqof6cxLMLRb0rJAwgZpPumKPTAGLUhJ9NrR0FBKjJBlvSigFAT3mA+Vb3LMab96uLeDoye4vj8EvCGj7AVClI/DjK/C6chxTMAa1fNtjQlJ3JMDPXEH1mfWGtMYJucrirte4Ug+BAd4k89gceTqRsd6EkmS9SfsSadcgrjc7QonpzJnjrFgTuSmKuOpSp4QaG/YmSexTpgnwynrg/hfU5Au+K7Ak6D2U9zpnnqD6LOOE5EIzzDWp+9a4Unu5juIF0FHNNzNBsUSMhXiN5eK06xvAywd4ak6GQonpzMbKYCMGrbsjZTk6/Ad4Zj4QVBCVLcexMngEFjc5hm9faKRcb4YVSnqrP2fAmm9mgmKJGAvpki69rOTi8Nh0zWWYAUxnzhpPL+BoGvRQd0eSKsTKVLYZfJJvoe6Of6PJzqHwuRMHQ6O3+nMGq/lmJiiWiHGQFiYSWCo8OgGo9XSmqzKdOWs8vYCjKdBT3R25Yff6CWg1UrP6yv/r582BmL0wLHqMEzJQzTczQbFEXEaemmye2gwsfkV73WQg0LC/3dXpYsoenlzA0fDoKZ7GiriAmg0Bnv8FCCsNXDkOfNEa2DUfhnRT6jVOSO9xpSaE2XDEJeQpK+3KCWB+DyA5AajcAXhkjOGKJOoZZrB5QDyNq+vulLofeOl34PsXgKOrtOr6p7cA7cYBvrbV4nWd9m+NE1LZcF4ZVxBjnJBHQMsScTp5ykq7cxOY3xO4eREoXht4Yka2Lkx0MeUMZrAZEL3F06QnuCDw7EKguVi/APw1E5jdHrh2xlhuSsYJEYol4mzylJUmmW8/DwbO7wFCimgZNzloiksXEzE12YyTWRfjk3O3t6OQB5uH3wV6LAQCw4Gz27Q4ppMbjeWmZJyQx0M3HNFv4cO/ZgG7vgW8vLWGnip2IGfQxUQcia6qwWfRzzFFrLeWQui1yhcpqza5txjrfW00t9x3PYGYPcDcTkCnT4C6zxrHTWmg3pDE8VAsEaeS66w0eQKVpz6h1Sig3EN5djERkhd0Vw3eTjyN1Yg0OvE5pNx1IFjd3m6zqhYoqwV+S/zSgaXATwOACweB1u85J+ZH725KYijohiNOJVdZaQnxWmBo8h2tpcKDbzhvBwnJBrqtBp9JPE0MCuGVxEH4JaWhvoqxihv9qS+Bh4Zq0xumaMkbCdc9I+2fGBaKJaK/woe//gu4fExLye3yP8CLLjPiPnRfDT5NPM3fTSej+51/o2nCJzZCSVfFWL29gZb/0mJ/fAKAv6OAmW2Ba2cd+zl6TfsnhoRiiTiVHGelHVwObJujLe06DQgq4NodJu6tp6VDDFEN/m48zYHCbbAppVqq603XxVilRlDfFZplJ3YfMPMR4Px+x22f7UGIA6FYIk4n21lpNy5qDXKFBwYC5Zu7YW9JXhB3VNPxq/HMjE14Y/5ONZZpIzctNlI1eMMVYy1ZH3jhN6BwZS3YenY74MR6x22faf/EQTDAm7iEbGWlRQ3X6ikVrQ60HOHO3SW5yAKzxvWktyO5PbDYgwSIIYuxhpcGno8Cvn0GOL0J+Kor8PgMoHpXx2xfBJH0kJSsNwnmFkuWuN5oUSI5gGLJ05EaIy66iNjNSjvyG7BngVYmoMunrq/yS/KUBZZVXI/cpGW5CGajlW3IrgBJSbEo16M7SwpY3d4iTjOpN63PYqxSwLLXj1pix8FlwMI+QPx4oNFLjtk+0/5JHvGyWKTyH8kLcXFxCAsLw7Vr1xAaGgrDkJc2AI7kzg3gs8bA1VNA4wFaSwSiCzKzFllvtVZrkcQmicstK77t39iQZRys3wMyECAyHR7sh6s3E/VRUkCPZQ5y8vC2Yiiw9Qtt+uF/Aw+9xSQP4vb7Ny1Lnoq1DUD626C1DYAr/fm/j9eEUlgp4OF/ueYzSZbkxFpkpLievMTdpRcgIpKu3Ey0EUp6cD0athirWIAenQiEFAXWfgisGQsk3tBqrVEwETdCseSJZNkGwEsrCCl+fmf79S8dBTZ+pr2Wi2RAPud+HnFKFpiR4nocJUAK5wvAkAU7M1w3u65HZ1YEN2wxVhFFLd7RajJJGZF1/9V6RKrMNeYkEfdAseSJ6Klb+cqRQEoiULE1ULmdcz+L5IicWIs61oo0XmBxHgWIuB5j4hJy18rHyK4yVyEZsf7BwLLBwJbPNQtTpykMzCZugTLdE9FLG4Djf2jBnF4+QJsPnPtZJMfkxFqU43paJiAvrkfdVgTXGw2eBx6briV+7PgaWPxy9hrfEuJgKJY8ET20AUhJAX55958LYtEqMDJmK8SYm+rr2a6nZRJy63rUfUVwvVG7O/DUHMDbV8uYlVpscv0wCyL+jv8J7FmkjSkGdQndcJ5IFt3KtTYAkc5tA3BgidZ9PCAUaDEcRsas7pTcpKEbNrDYhTWNchILZsiYI2dQrQvw5CxgYV9g5zxNOHWcbPwYJr1kJJMsMdwvberUqShbtiwCAwPRqFEjbNmyJdN158yZAy8vL5tB3pcWqZwwcuRIFC9eHEFBQWjdujUOHz4MU+PuNgDy5CSZLkL5FkDsfsM+TZndnZIba5E1rqdLnRJqbEahJOTW9Wj2zEGnCqbH/09zyW2fq5UYMHLlG2tGcvr4UWtGsiwnusFQYum7777D4MGDMWrUKGzfvh21a9dG27ZtERsbm+l7pG5CdHR06nDy5Emb5R9//DGmTJmC6dOnY/PmzQgJCVHbvH3b5Bcqd7YB+PXfwIVD/1iY5nYEJtcw3MXBU9wpIojWvdNS1Uj6pHsdNZZpI1vN3CkmPSFz0GnUfBIpnafCInJ06wycWzAYyckpJsxIliexYYZ9iDQjhipKKZak+++/H//73//UdEpKCkqVKoXXXnsNw4YNy9CyNGjQIFy9ejXD7cmhR0ZGYsiQIXjrrbfUPClMVaxYMfXe7t27m7sopYsreCv2/ggs6p3BgrtP3wbq12T2Qowk++SkBICsK/3ysnLfiSA1q1Uury7vZvEr8LHfDDXv/3y6o/Rj7xlLvEtskjwkZkXvZaw87mSye/82jGXpzp072LZtm3KTWfH29lbTGzduzPR98fHxKFOmjBJVXbp0wb59+1KXHT9+HDExMTbblC9NRJm9bSYkJKgvOO1gWKxtAKQDuIydKZREmP08KJOFxnuaojuF5Mb16ImZg452eS9IfhgjE7WHrheT5+OPbycYy+Wtl4xkkm0MI5YuXryI5ORkZfVJi0yL4MmIypUrY9asWfjpp5/w9ddfK0vUAw88gDNnzqjl1vflZJvCuHHjlKiyDiLESDYQC9atK9mr72QAXOFOMWOWHfG8zEFnuLy/TG6LKUlas933fWdh7Y+zjPP/oYeMZJIjTJ0N16RJEzVYEaFUtWpVfP7553j//fdzvd3hw4er2CkrYlmiYMoGpzK31uXlacqZVZDd2eHdrFl2xPMyB/NKZhmEk5KeQmFcQw/fNRidOAkHN9ZB9Qc7QPfoISOZmFMsFS5cGD4+Pjh/3vZGKtMRERHZ2oafnx/q1q2LI0eOqGnr+2Qbkg2Xdpt16tTJdDsBAQFqILkoQungpyl3CgpndnjPrIGtu3uOEcdi2JYkLiZzV7YXRiQ9j0Je19HW5y/ct+ZFoMIvQEQNGCIjWfXnzOTq4cyMZGJeN5y/vz/q16+PVatWpc4Tt5pMp7Ue2UPceHv27EkVRuXKlVOCKe02xUokWXHZ3SbJJtfPZ8O9Jk9TJbL9NKWHtH1nuFM8JcuOkOxiz5WdDB+8njgQm1OqwC8pHvimm3a90TvuzEgm5rUsCeL66t27Nxo0aICGDRti8uTJuHHjBvr27auW9+rVCyVKlFAxRcKYMWPQuHFjVKxYUWXETZgwQZUOeOGFF9Ryqbsk2XJjx45FpUqVlHgaMWKEypDr2lXzhRMHsesbwJIMFKqoNc9V5P5pKitBkZ0mpnp1p7BoISE5c3nfgT9GBL6LX/KPgdelI8D8Z4A+PwN+QdA1IoikYbkrM5KJ+cVSt27dcOHCBVVEUgKwxVUWFRWVGqB96tQplSFn5cqVK+jfv79at0CBAsoytWHDBlSrpmWiCEOHDlWC68UXX1SCqmnTpmqb6YtXkjwg1Sm2f6m9bvqmVrU7w6q1H2X7aUpvgsKR7hRm2RGSc5f34M4N4VV8AfBFK+DsNq2P3JOz3VblO9uxlNaMZKJrDFVnSa8Yus6SK2uK+OcHhhwEAvLlub6TZIe9MX9nlutJEUVJ5zYSrN9ESB5iFE+sA77sCqQkAs3eAlqN0Od+EkPdvw1lWSIGRXo5CTWf0ISSA56mzFwF2dlZdoQYlWy5vMs2BTp9Avw0APhzIlCkClDrKZftI5MzzIlhAryJQUlKAA7+rL2ulb2K6DkRFJlFBcn84gYVFCxaSEgeC4DWfRZ48G4B3CWvATF7XbJvTM4wLxRLxLkcXQ0kxAH5I4FSjRy2WbMLChYtJCSPtBoJlH8YSLoFfNcTuJVx2ytHkpNYSmIs6IYjzmXvD9q4eleHB1paBUX62IAIk8QGsGghIXlAXP1PzgI+bw5cOQ4sfgno/q1TA76ZnGFeKJaI80i8DRxaob2u/phTPsLsgoJFCwnJA8EFgW5fAbPaAn9HAX9MAFq847SPM3MspadDNxxxHqc2AHeuA/kigBINdNHElBDiYUTWATpM0l6vHQccXeO0jzJzLKWnQ7FEnMfh37RxpdZuq3VCjA+bCZM8IwHf9ftoUUPijou/4JSPMXsspSdDNxxxHkfuiqWKj7h7T4hBYb0a4jDajgNObQYuHAB+fAXoscApD3Fmj6X0VFiU0gGwKGUGXD0FTK4JePkAQ48BQeHu3iNiMDKrV2N9JmdWIMkx5/cDMyRD7jbQ9kOgyavur+BNDHH/pm+EOIdja7VxyfsplEiOYb0a4hSKVQPafqC9XjkKOLfDaR/FWEpzQbFEnMOpu+06yj7o7j0hBoT1aojTaNAPqNJRa4fyfX8g8Za794gYAIol4lyxVLqJu/eEGBDWqyFOw8sL6PyplqV76TCweqy794gYAIol4njiY4HLR7XoEnHDEZJDWK+GOL3+kvSPEzZO/efhjvyDNDuXJuh7FmljmfZgmA1HHM/pzdq4aFXGK5FcwWbCxOlUbgfUeVZr9C3ZcS+vB/yD3b1X+mD/EiDqHSDu3D/zQiOBduOBap3hidCyRBxP9C5tXKK+u/eEGLRWEevVEJcgGXHSt/LyMWDVaHfvjX6E0oJetkJJiIvW5styD4RiiTie8/u0cURNd+8JuZuC33T8ajwzYxPemL9TjWVa5usZNhMmTkcs3xK/JGz+HDi9FR6NuNrEomQvDzVqmEe65OiGI44nZq82Llbd3Xvi8WRWq0jcWzJf76LD7L3/iA6QDgO1ewC7vgGWDQJe/B3w8dBb48kN91qUbLAAcWe19co1gydByxJxLLeuAtdOaa8pltyKWWoVsV4NcTpt3geCCgDn9wKbp8NjiT/v2PVMBMUScSwXDqqRJbQkNp5LMUyMjBlhrSJCsklIYeCRMdrrNR8CV0/DI8lXzLHrmQgPtTUSp3H5uBptu15AxcZYYT8v18NaRYTkgDo9gZ3fAKc2anE53efB4yjzgJb1JsHcmeWhhkZq63kYtCwRh3Lk0B41PpxYOMMYGb0HFZsJ1ioiJAdIU92O/wW8fYGDy/5pBO5JePto5QHs5aG2+0hbz8OgWNIrBiwIJq62w4e04O7TlqKGjZExS5q+tVZRZhE+Ml+Ws1YRIfinNlyjl7XXv/wLSE6CxyF1lJ7+EghN5wUIjdTme2idJbrh9IhBC4JJ7EvhpGglwU9aitmNkZFAXTMiljMRhGljhdzlgrTWKhKLngijtJKNtYoIyYSH3tbccRJ/uX0OcP8L8DjkPlOlg5b1JsHc+YpprjcPtChZoWVJbxi4IJjEvhT30oKFoy0FPS5Gxpqmnz6o2p0uSNYqIiQXtZcefld7vfoDLcPXExFhJOUBaj6pjT1YKAm0LBmqIJiXFngoil+HP9yi+QJQCHHq9QWEeVSMTFZp+mK7keVSM8jVlhzWKiIkh9TvC2yZAVw8BPwxAWj7gbv3iLgZWpaMWhBMh7FPDUsGIMjrjnp9yRLmUTEyek/TZ60iQnKAFKW0CiSp7H31bu044rHkWCwtX74cL7zwAoYOHYqDB7WaOlauXLmCli1bOnL/PAtHFwQTl93kGsDcjsD3/bSxTDvJledz86Ia37L44xYCbJaZPUaGafqEmIxKjwBlmwEpiZp1iXg0ORJL33zzDTp37oyYmBhs3LgRdevWxbx5/9SiuHPnDn7//Xdn7Kdn4MiCYO6IfbpxMbXAW0RYkEfFyDBNnxAT0nKENt4xD7h01N17Q4wSszRhwgRMmjQJr7/+uppesGABnn/+edy+fRv9+vVz1j56Do4qCOau2Kc719UoKF8BrHu5pUfFyFjT9CWYO5MzpwSjGV2QhJiW0o2ASm2Aw78Cv48HHv8/6AK5xjNTTb9i6fDhw+jUqVPq9NNPP40iRYooa1NiYiIee+wxZ+yj5xUEE8tPZsne2SkI5q5miIl3XUx+gakxMp4C0/QJcX+ShVMe0CQzTsTS7gVA0ze1WkzuxKClZTxKLIWGhuL8+fMoV65c6ryHH34Yy5YtQ8eOHXHmzBln7KNnFgTL8J/ho+z9M7irGWLSLW3sa+uC8xSsafrp6yyJRYmtXggxaH2zyLpA1U7AgaVa7NKTs+A2rOEV6e3X1vAKDy4aqSux1LBhQ6xYsQKNGze2md+8eXMsXbpUCSaig4Jg7mqGmMay5KkwTZ8Q99Q3S+/+ttY3c0is5ENDNbG0b7EWx1TwH4OByzB4aRmPCvB+8803ERiY8Y2wRYsWSjD16iWql7i1IJg19sleo4vQEo5vhmi1LPl5pmXJCtP0CdFHfTOHtVgqXguo0AqwpAAbPoVpSssQ54glsSANHz480+Xikps9e3bq9EcffYSrVz20+qknNkO0WpY81A1HCDFxfTOJVxJ2zgPiY+Fy3BVeQZxflPLDDz/E5cuOLcI3depUlC1bVlm4GjVqhC1btmS67owZM9CsWTMUKFBADa1bt75n/T59+sDLy8tmaNeuHQyPG5oh/uDVCt0CP8N/LM86fNuEEOLW+mZlmwIlGgBJt4HN0+Fy3BVeQZwvliwWx3Zb/+677zB48GCMGjUK27dvR+3atdG2bVvExmas8teuXYtnnnkGa9asUXWhSpUqhTZt2uDs2bM264k4io6OTh2+/fZbmAIRRIP2Ar2XAU/M1MaD9jgtAPC6JQibr4bjaEJ+p2yfEELcVt/MywtoOkh7vXUmcOcmXIq7wiuI8dqdSI2n/v37o2/fvqhWrRqmT5+O4OBgzJqVcXaCFMwcMGAA6tSpgypVquCLL75ASkoKVq1aZbNeQEAAIiIiUgexQpkGFzZDDPTTfk63E1Oc9hmEEJK+vpkd+eDYFkuVHwXCywC3rwJ7F8EjwiuIscSSVAfftm2bcqVZ8fb2VtNiNcoON2/eVPWgChYseI8FqmjRoqhcuTJeeeUVXLp0ye52EhISEBcXZzMQEUvaP+ntROf1nyOEkPT1zezIB8fWNxMhcv/dAszSaNfB3hM9hlcQg4mlixcvIjk5GcWK2fpjZVrar2SHd955B5GRkTaCS1xwX375pbI2jR8/XrVrad++vfqszBg3bhzCwsJSB3HvESDA19hiSTJmNh69hJ92nlXjPGfQEEJcVt9M6pm5pMVS3ecA30AgZjdwZivMHl5BclFnychIZt78+fOVFSlt+YPu3bunvq5ZsyZq1aqFChUqqPVatWqV4bYkI1Bip6yIZYmCydhuOKcWtSOEmKe+WXBBoMYTWlacWJdKNYTbwiuIOSxLkokWFOSYNPLChQvDx8dHVRBPi0xLnJE9Jk6cqMTSr7/+qsSQPcqXL68+68iRI5muIzFOUs087UCAYH9Ne99ygWXJkVYga1G79CnI1qJ2spwQom9cWt/s/he08f4fgZuOzfgmJrQsSRaaDBI0nRarIFm+fDkchb+/P+rXr6/cZV27dlXzrMHaAwcOzPR9H3/8MT744AP88ssvaNCgQZafIy1bJGapeHFaE3JKgWA/Nb58445hrEBZFbWTy60sl6dWFpckhChK1AMiagIxe4C93wMN+7t7j4geLUsSaF2jRg0lKEQYSbZZ3bp1U8fOQlxfUjtp7ty5OHDggArGvnHjhsqOE6R6eNqimRKDNGLECJUtJ7WZJLZJhvj4eLVcxm+//TY2bdqEEydOKOHVpUsXVKxYUZUkIDmjUL4ANb52KxF3kpzjinO0FcilRe0IIeahdg9tvMskpWaI4y1Lzz//PO677z7MnDlTBVhLIUdX0K1bN1y4cAEjR45UokfEWVRUVGrQ96lTp1SGnJVp06apLLonn3zSZjtSp+m9995Tbr3du3cr8SWVxiX4W+owvf/++8rVRnJGeJAfxPgiHrErN++gWKhje8Q5wwrk0qJ2hBDzUPMpYOUI4Ow24MIhoEhld+8R0ZtYOnbsGL7//ntlgXE14nLLzO0mQdlpEWuRPSSeStxzxDF4e3uhYIg/LsbfwaV4x4ulnFiBJGZBd0XtCCHmIV8RoOIjwN8rgJ3fAI+MdvceEb254SRLbNeuXY7fG2J4CoVoFrkL8QkO37YzrEAuL2pHCDEPdZ7RxnsWShCtu/eG6M2yJJWwe/fujb1796rYJT8/LbDXSufOrPfgqZQoEIRD56/jzBXHtwJwhhXIWtRO4p1EGFmcXdTOTYgL0yVp1YR4EpXaAv75gLizwLntQMmsk4iIB4klqZi9fv16rFix4p5lEr9kr6AjMTelCwar8enLtxy+basVSIK5M4pb8rpbiC6nViBrUbv0GXYRJqmzxBpShDgJv0CgUhtg3w/A/p8olkxMrtxwr732Gnr27Kmazkr6ftqBQsmzKVlAq6t1+vJNQ7U2ENGw7p2W+LZ/Y3zSvY4ay7TRxQRrSBHiZKyVsw8scX37E6JvsSR1iN588817Wo8QkmpZcoIbztmtDVxa1M4FZJU9KMhytnUhnopDittKkLe0P7lyAji/1xm7SYzqhnv88cexZs0a1RaEkLSULqSJpeMXb8BisTilrIRLWxsYGGdkDxJiFhzmng7IB1RsDRxcBhxYphWrJKYjV2JJaixJ8cd169apfmrpA7xff/11R+0fMRjlCoco0XL9dhJi4m6jeJhj2t1kZgUimcMaUoTYd0+ntyNZ3dM5tlLf104TS0d+Ax7+pzAyMQ+5zobLly8ffv/9dzWkRSwJFEueS4CvD8oXDsHh2HgcjL7uNLFEsoY1pAhxUYujCi21sWTESa84abZLTEWuxNLx48cdvyfENFQpHqqJpZjreLhKUXfvjsfirOxBQoyMU9zTYSWAIlWACweBY2uBGo87boeJcQO8CbFHlYj8anwoJs7du+LRODN7kBCj4jT3dIVW2vjoqlzsFTGlZUk4c+YMlixZovqxSf+1tEyaNMkR+0YMLpb2naNYcjdmryFFiG7c0xVbApumAkfXaCUEXNQzlehYLK1atUpV6S5fvjwOHjyoqnhLHzbJfqpXr57j95IYitqlwtVYXHHXbiUiLMg2AYC4FmYPEuIC93TpJoCXj1bN++opoEAZR+0yMaobTjLh3nrrLezZsweBgYGqqe7p06fRvHlzPPXUU47fS2IoCucLSK23tPP0VXfvDjFhDSlCdOee9g8BitfWXp/e7IA9JYYXSwcOHECvXr3Ua19fX9y6dUtlx40ZMwbjx4939D4SA1KvtGZd2nHqirt3hRBCXFPctnRjbXxqkwP2khjeDRcSEpIap1S8eHEcPXoU1atXV9MXL1507B4SQ1KvTAH8uPMctp+iZYkQ4iHu6VKNgE2f0bLkSFKSgZMbgPjzQL5iQJkHAG8fGEIsNW7cWBWkrFq1Kh599FEMGTJEueR++OEHtYyQeqULqPH2k1eQlJwCXx8mXhJC9IXDi9taLUvn9wG344DAUMdt2xPZvwSIegeIO/fPvNBIoN34f3ryuYhc3cEk261Ro0bq9ejRo9GqVSt89913KFu2LGbOnOnofSQGpGrxUIQH+yE+IQm7zlxz9+4QQojzyR8B5I/UqjWJYCJ5E0oLetkKJSEuWpsvy/VsWUpOTlZlA2rVqpXqkps+fboz9o0Y/IntgQqFsHxPDNYdvoj6ZTRLEyGEmBrpDXf9nNZUt0wTmJYUJ7rHZNtiUbJXZz1qGFClg8tccjm2LPn4+KBNmza4coWBu8Q+TSsWUeN1Ry64e1cIIcQ1RNTQxjF7YFr2LwEm1wDmdgS+76eNZdpR1h4RYektSjZYtBINsp6e3XBSV+nYsWOO3xtiKppVKqzGO05dVe44QggxPcVMLpb2u8A9JtYqR67nLrE0duxYVWdp2bJliI6ORlxcnM1AiFCqYDDKFApGUopFueIIIcT0RGghKojdr7mTzERKVu4xaO6xvB63uPUcuZ67xJJkwO3atUtV8S5ZsiQKFCighvDwcDUmxEqrKtqPeeV+1z0BEEKI2yhQFvD2A5JuZ+FKMiAnXeQek/gnyXq7p2yoFS8gtIS2np5LB6xZs8bxe0JMSdvqxTBr/XGsOnieJQQIIebHx1drdXLpCHDlOBBeCqYh3kXuMQnalvIA4tZTgimtJeuugGr3kUvrLeVKLElbE0KyQ4OyBVEwxB+Xb9xRxd8eqKjFMRFCiGkpUE4TS5ePAeUegmnI50L3mNRRevrLTOosfeTyOku5EktWbt68iVOnTqVW87ZiLStAiJQQaF21KBb8dQa/7j9PsUQIMT8Fy2vjy8dhKsrcdY9JMHdmbYhluaPcYyKIpDyAUSt4X7hwAX379sWKFSsyrcVEDIyD62e0qRahxNKKvdEY0TEXDSoJIcRIFCynjcUNZya83eAek22Va+a47eV2N3LzpkGDBuHq1avYvHkzgoKCEBUVhblz56JSpUpYssS1VTWJ/utnNLuvMMKC/HA+LgGbjl2C0UlOsWDj0Uv4aedZNZZpQghJRYKPhesxMB3V7rrHQtM1GxaLksx3sXtM15al1atX46effkKDBg3g7e2NMmXK4JFHHkFoaCjGjRuHDh06OH5PievqZ6Q3r1rrZ+TyHyHA1wcdahXHN5tP4YftZ/GggV1xUXujMXrpfkRfu506r3hYIEZ1qpb7TuVpEOHl0MaehBDXY43ZcWEdIJdSTT/uMV2LpRs3bqBo0aLqtZQKELfcfffdh5o1a2L79u2O3kfiCpxcXv7xuiWUWBKxMbZrDQT5G++fSvb9la+33/MNxVy7reZP61kvT4LJ2UKMEOIi8mndCxAfC1gsgJcJH3i89eEe07UbrnLlyjh06JB6Xbt2bXz++ec4e/as6hFXvDgv6obEyfUzpDdcqYJBuHEnGb/uN55pWiw+ImTslGJTy3PrkrMKsbRCKa0Qk+WEEIMQohkTkHgTuBPv7r0h7hJLb7zxhqrcLYwaNUoFepcqVQqffPIJPvzwQ0fsFzFZ/QwvLy88Vkfz4y/adgZGQ1xj6YVMWkQiyXJZT29CjBB34bHxfQH5AL+Qf6xLZvA8HP8T2LNIG5utMrmz3HA9e/ZMfV2vXj2cPHkSBw8eROnSpVG4sHHjUTwaF9TPeKpBKXy65gj+PHwRRy/EIzYuwTCxObKfjlwvt0KsSYVCOd4+Ie7A493KgaFA4g0g4ToMH8salVGto/GmDebOiFyXU545c6ZqqBsYGKjilnr16oUff/zRsXtHXIcLystLr7jm92m+/M6frsMzMzbhjfk71bjp+NW6djWJoHPkeq4SYoS4A7qVAfjn08ZGdsO5ommumcXSyJEjlSuuU6dOWLhwoRrk9ZtvvqmWEQPXz1CkF0yOq59RrXh+NZbYJSNdRMXyJU/FdqSkWi7r6UmIEeJq6FZO44oTEgwqllzVNNfMYmnatGmYMWOGKhMgzXRlkNf/93//h88++wzOZOrUqShbtqyyaDVq1Ahbtmyxu74IuSpVqqj1JVtv+fLlNsstFosSeBKYLjWjWrdujcOHD8MjcXL9DLk4SukAGPAiKi5CcR/YkZJqeW5cic4UYoSYKb7PUBjdsuSqprlmFkuJiYmqxlJ66tevj6SkJDiL7777DoMHD1ZB5VKiQDLx2rZti9jYjAPoNmzYgGeeeQb9+vXDjh070LVrVzXs3bs3dZ2PP/4YU6ZMUZl8UmQzJCREbfP2bQ91eYggGrQX6L0MeGKmNh60xyG+abk4xsQlGPYiKnEWUh4gIszWwiPTeSkb4Cwh5rHBtcSt0K18F9+714mkzK95usZVTXMNgpdFTCs55LXXXoOfnx8mTZpkM/+tt97CrVu3lPXHGYgl6f7778f//vc/NZ2SkqKy8GR/hg0bds/63bp1UzWhli1bljqvcePGqFOnjhJHcuiRkZEYMmSI2nfh2rVrKFasGObMmYPu3btnuB8JCQlqsBIXF6f2Q94rhTlJxshNW2KUsuKT7nXQ5W7mnB5xVuFIRwbEenxwLXEbIswlDjErvu3f2NwJC98+AxxaDnT6BKjfB4ZDst6kg0NW9F5m6HpLcv8OCwvL8v7tm5cA719//VWJD0GsMtJUVwK9xfpjJb2gyi3SrHfbtm0YPnx46jypHi5us40bN2b4Hpmfdl8EsRpZA9GPHz+OmJgYtQ0r8qWJKJP3ZiaWxOU4evRohxyXJ2GW2BwRRs64yIuIeaRaRJ6FmLOLZxKSHbey/N4yabWqrLFmdyvfTvGGXMnES6HvK5pOmubqnFyJJXFjSckA4ejRo2osJQNkSOvikto6juLixYuqQa9YfdIi01K2ICNECGW0vsy3LrfOy2ydjBDBllaEWS1LxD68iDpfiGUVXCvfsSwXUabnUg3EuFjdyiLMM2m1muv4PiM0DrfyXmIvrE9ohVdSGqEHDIg7muaaTSytWbMGnkxAQIAaiMkvogaENZuInuL70ruCI/TmCnZiDaGLXoVw2pIErwAtA9jQST9RGX1HH3lUnaVcu+FcjVitfHx8cP68bTCZTEdERGT4Hplvb33rWOalbdMi0xLXRFx3ES2SPwBjulTXz0XUoDC4lugFR7mVjdY43EpSSooa++rleHOLBzbNdWhRSlfj7++vsu1WrVqVOk8CvGW6SZMmGb5H5qddX1i5cmXq+uXKlVOCKe064lKT+KvMtkkccxFd905LFeBZqaiWXtuqalEKJQdglrgwYi63siRsyFg3QskFNYQSEjWx5O9rmNts1k1zaz6pjT1MKAmGOosSJyT1nebOnYsDBw7glVdeUdluffv2VcsluDxtALgUzoyKisJ//vMfFdf03nvv4a+//sLAgQNTY6oGDRqEsWPHYsmSJdizZ4/ahmTISYkB4vyL6AeP1VTTC/86g9OXb7p7twwPazYRoo8aQjfuaGV08gUYxoFDzCKWpBTAxIkTVRFJcZPt3LlTiSFrgLZk41kb/AoPPPAAvvnmG1UsU2oyLVq0SGXCSZsWK0OHDlWlB1588UVVliA+Pl5tU4pYEucjN+2mFQsjKcWC/60+4u7dMXzzSmcWzyTENLighlB8giaWQiiWPLfOEsldnQaPxk7GybaTl/HEtI3qBr56SHOUKXS3W7cnk8fAU9ZZIsS9NYQaffgbzsclYNlrTVGjRFiutkFMUGeJEEfd+OuXKaga7P7+9wX8d+XfmNy9LjwaBwSe6j64lhAT1xASG8S1W4nqdf5A3mbNgKHccMS8XavfbltZjX/ceQ67z1yFx+LAwFPdBtcSYvLG4dIo/PbdAG/J9CXGh2LJk8lFTIyzbvxipn68rtbiZOzPB9STmUfC5pWEGL5xeGzc7dTg7mB/X/Ne4z0I2gc9FScWY8vVjb9cM7zVtjJ+3hOtXEcr959Hm+oZ188yNWxeSYjhawhduJ7gfquSK67xHgQtS55INl1jrr7xR4YH4YVm5dTrj1YcRGKyZsb2KORi7cj1CCEuryEUaxVL+QLMfY33ICiWPA0XFGPLy43/5eYVUDifP45dvIG5G07AYwNP7VVKCi3hMc0rCTEip69oNeNKFggy9zXeg6BY8jRcGROTixt//kA/vNVGC/aWzDhpuutRODnwlBDifE5cvKHGbimDwrhHp0Cx5Gm4MiYmlzf+pxuUQt3S4Sqj5P2f98PjcGLgKSHE+Zy4pFmWyhYOdv2HM+7RKVAseRqujonJxY3f29sLY7vWgGS6/7w7Gn/8fQEeh3wvg/ZqRfGemKmNB+2hUCLEALjVssS4R6fAbDhPw8nF2ByVcVI9Mgy9HyiL2etPYNSSfYga1AwBvj6eGXhKCDEMcbcTUwO8yxYK9oxrvAdAy5Kn4a6YmFxknAx+5D4UzR+A4xdvsG8cIcQQHDgXp8aRYYEID/Z3/Q4w7tEpUCx5IgaJiZFg79Gdq6vXn609ir1nr+V9oyzSRghxIvvuiqVqkW7sB2eQa7yRoBvOU3FSMTZH075mcXSoWVwVq3x70W789OqD8PfNpcZnkTZCiB2SUyx57qe4P9oqltzcVN0g13ijQLHkyRgkJmZ0l+rYeOwSDkTH4bO1RzCo9X1uaU5LCDEvUXujMXrpfkSnKVdSPCwQozpVU42pc2pZqu5usWSga7wRoBuO6J7C+QJS3XESu7T/7sUo27BIGyEkC6H0ytfbbYSSIHXeZL4sz25w96EY7fpUu2S4U/aVuAeKJWIIOtYqjrbViyEpxYI3v9uJ24k5EDYs0kYIseN6E4uSnUcptVzWy4ptJ69AVitdMBgRYYEO31fiPiiWiCHw8vLCB4/VVK1QDp2/rnrHZRsWafN45Ea38egl/LTzrBpn58ZHPAOJUUpvUUqL/FJkuayXFVvvrnN/2YIO3UfifhizRAzljpvwVG30nb0VczacQPP7iuDhKkWzfiOLtHk0jopFIeZEgrkdtd7WE5pYalSOYsls0LJEDMXDlYui74Nl1eu3F+3ChbvF3+zC5rQei6NiUYh5kaw3R6wXn5CEnaevqteSRUfMBcUSMRzvtKuCKhH5cTH+DoYs3IWUrFwqLNLmkTgyFoWYFxE2Ymm08yillmclgNYfuYjEZAvKFApWAzEXFEvEcAT6+WDKM3UR4Out+sZJOYEsYZE2j4sdcmQsCjFvDJnUURKXrJ1HKbU8q3pLaw7Gplq/JcZSr8dLcgdjloghua9YfrzfpQaGfr8b/1n5N2qXCkezSkXsv4lF2jwqdsiRsSjE3DFk8rnTeta7Z78isrlfFosFaw7dFUvp4ij1eLwk53hZ5CyTPBEXF4ewsDBcu3YNoaE6KETmQbyzaDe+++s0Cob4Y9lrTREZHgRPrRxshNih9Bcb6xHKjcrRNw55gn9mxqYs1/u2f2M0qVDIoZ9N9PM7cMX/obRi6vjpOgT5+WDHyEeU9dsIx0uQ7fs3LUvE0Eh1773nrqmquQPmbceCl5rkvh2KmzD7k2dWsUNy45Dlj1SLcKhAtMaiSDB3Jr3XleWAwbjm/h3kBPnc3Ajnpbu1Om4tKhdJFUpGOF6SfYx1VyEkHXJhmt6zPkIDfVUmyphl+2AkPCFby12xQ46KRSGOwawxZOKcWbZL+z/tXFuybg10vGwsnm0olojhKVUwGJO714HEVH696RS+2nQSRsBTsrXcGTtkjUVJX01ZpukCcS1mjSHbfuoqzl69hRB/H5t4Jd0fr/TLnFwDmNsR+L6fNpZpmU/ugW44kjvkCURHgdItqxTDW20qY8Ivh/Dekn0oXzgED1YsDD2TkyfP7LgG9Br35Kg6NrlFBJG4OvT43XgS7v4dOIuluzQXXJvqEakuON0fLxuL5xiKJZK7fzRpTJu235qk4EstIzf+gw1oUQFHYuOxeMdZFb+0eMADKF8kH/SKI5889Rz3pIfYodzGohBz/Q4cjfSo/HHnWfW6c51/XHC6Pt4sG4t7aY3FJXOYmcKp0A1HcvdEkr4xrfWJxI0mXKltMu7xmqhbOhzXbiXihbl/4drNROgVRz156j3uibFDzsUoNXzM+Dv4ZV8Mrt5MRGRYIB5KV7pEt8fLxuK5gmKJOPCJRO7cw9waJChm8P97roG6eB27eAOvzNuGhKRk01YONkrcE2OHnIMI4abjV6sSCW/M36nGMu1ugewpv4NvNp9S4273l85Q9OjyeNlYPFfQDUec80RSrhncRZH8Afii9/14avoGbDh6CW8t3I1PutWBt86eWK1PnmL9kT2z5OLJ09FxT86EsUOOJbMaPlaLol7Fh1l+B0cvxGPz8cuQ3e52fynjHC8bi+cKiiViyieSapGhmP5cffSdvVUFYBbLH4B/d9RM4noir5WDdZ9xkw7GDjkGo9fw0dvvIDfJEXPWn0hNLklvOdL18Vobi0voRGbRVLKcjcVtoFgipn0ikfYnE56qhTe/24Uv1h1XF7QXmpWH3sjLk6euM26I0zCSRVHv5CY54vKNO1i47bR6/XzTsjAU1sbiKhsuE5s2G4sbN2bp8uXLePbZZ1U58vDwcPTr1w/x8fF213/ttddQuXJlBAUFoXTp0nj99ddVSfP0QcHph/nz58OU5LUAmfWJxF6UTWgJXT2RPFa3JIa3r6Jej/35gAqC1WMxN+uTZ5c6JdQ4u9YAR3VMJ8bCaBZFvZLb5IgvN57A7cQU1CwRhiblDShG2VjcvJYlEUrR0dFYuXIlEhMT0bdvX7z44ov45ptvMlz/3Llzapg4cSKqVauGkydP4uWXX1bzFi1aZLPu7Nmz0a5du9RpEWOmwxHp/gZ9InnxofKIibuN2etPYMiCXQjx90XrasVMUTrBEXFPxHjQoug+V+atO8n4cuPJ1GuLPGAbEjYWN18j3QMHDijBs3XrVjRo0EDNi4qKwqOPPoozZ84gMtK2vkVmLFy4ED179sSNGzfg66vpRPmhL168GF27djVvI93MCpBZb6c5fZLIUCyU0ISSTp9IUlIseHPBTvy08xz8fbwxs08D5aZz+3fpIPRcZ4k450YvWW9Z1fBZ905LCmUHN1qete44xizbj5IFgrD2rRbw9TGMg4aYvZHuxo0blbXHKpSE1q1bw9vbG5s3b8Zjjz2Wre1YvwyrULLy6quv4oUXXkD58uWV9UmsVvaeFhISEtSQ9svWLc4oQGbAJxLJhPvPU7WRkJiCqH0x6P/lX5jbtyEa2TGh3xP0WSYMPjot5ubsjBu9Vgf3VGhRdI8r8+adJHy29qh6PaBFRQolD8IQYikmJgZFi/7Tc0cQwVOwYEG1LDtcvHgR77//vnLdpWXMmDFo2bIlgoOD8euvv2LAgAEqFkrimzJj3LhxGD16NDw63V/EgBvLA+QGubBNeaYuXvrqL6w5dAHPz9mKr19ohLqlC2TLUvNo/iP4LFG/pROclXFDq5U+yWsmpaeTG1emuN8uxiegdMFgPNWgpBP3jugNt4qlYcOGYfz48Vm64PKKWH46dOigXHnvvfeezbIRI0akvq5bt65y0U2YMMGuWBo+fDgGDx5ss/1SpTKvs+FWDJTu7wr8fb0xrWd99Ju7FeuPXEKvWVsw74VGqFUyPMv6Nb43YgF/z/oujVrLx1PQXQ0fA5HTdiTXbydi+u+aVemNVpXgR6uSR+HWsz1kyBAlhuwN4hqLiIhAbGyszXuTkpJUxpsss8f169dV8Hb+/PlVbJKfn5/d9Rs1aqTioNK62dITEBCg3HlpB91isHR/V1X5ntGrAe4vWwDXbyfh2Rmbse3klSyDPmMR7lHfpVGqg3s6uc2k9HRy2o7k89+PqdYmFYqEoGvdEi7eW+LRlqUiRYqoISuaNGmCq1evYtu2bahfv76at3r1aqSkpChxkxli8Wnbtq0SN0uWLEFgYNZm1507d6JAgQLqPaaABcgyJNjfF7P7NlSuOHkq7zVzM2b1uR9y38+sfs2WlCo4ZymICGhVe83+XbKWDzE72XVlnr58E//35zH1emi7KhSkHoghYpaqVq2qrEP9+/fH9OnTVemAgQMHonv37qmZcGfPnkWrVq3w5ZdfomHDhkootWnTBjdv3sTXX3+tpq2B2CLQfHx8sHTpUpw/fx6NGzdWQkrKEnz44Yd46623YBoMmu7vsOB2O0Ho+QJ8Mafv/SrYW1xyvWdvwfMPlst8c/DG6MRemOY3GRZ4wcvk3yVr+RBPIDuuzA+XH8CdpBQ8WLEQ2uSl7AgxLIYQS8K8efOUQBJBJFlwTzzxBKZMmZK6XATUoUOHlDgStm/frjLlhIoVK9ps6/jx4yhbtqxyyU2dOhVvvvkmpIKCrDdp0iQlykyFtQBZhrWB9JvunyeyWQtJLEwze9+Pl7/ehrWHLmDG3afHzPglpSFeSRyEKeHzEXAzxtTfJWv5EE/BXnLEhqMXsWJvjLImj+xY3bh1lYj56yzpHd3XWcqmpcU05KIWUkJSMgZ+swMr99sPzk6tX/N2c/ic3mjq75K1fIinI9eFjlPW4XBsPHo1KYMxXWq4e5eIm+7fDOf3JKzp/jWf1MYmu7lnr66UpHgNu6c9SYCvDz57th461c68wKlN0KfU6jL5d5nTAFhCzMZna44qoVQ4nz8GP3Kfu3eHuBGKJWIuclJXKh2SCvxJtzro80DGjTHFiuJpqfLWANj0XdU98bsgnsXf56/js7VH1Ov3OldHeHB26oYQs2KYmCVCXFFXSip9i7WkaGgAPo46pOZJsOdrLSvigQqFPdKKwlo+xNMQF/TQRbuRmGxB66pF0aEmHwo8HYolYi4cUFdKAjillUHhfAEY/sMeJRK++PM46pQKR/5A+3W6zIqzqoMTokc+/+Modp6+qjJm3+9ag0HdhG44YjKsdaXuibJJWwupRLZqIT3doBT+77n6CPTzxu9/X8AT0zaoeiuEEPOy+8xVTPr1b/V6ZMdqKB4W5O5dIjqAYomYC2tdKXthyTmohdSqajEsfOkBFM0fgL/Px+Oxz9Zj+ymt2jchxFxIo9w35u9EUooFj9aMYP83kgrFEjEf1rpSoeniDMTilEHZgKyoWTIMPw18ENWKh+Ji/B10/79NWLLLXhA5IcSIjFm6H8cv3lA94z58rCbdbyQV1lnypDpLnoaD60rdSNCeOn87oAWHv96qEga1qqSCwgkhxmbhX6fx9qLdEH0kzbUloYOYnzjWWSIej4PrSoUE+OLz5+qjfzOtJcqUVYdVq5RrtxIdtMOEEHew9+w1/OvHver1G60qUSiRe6BYIiSHWWH/6lAN/3mqNgJ8vbHqYCy6/G8dDsVchyktc8f/BPYs0sbpCnkSYgau3LiDl77apnq/tapSFK+3rARDw/9bp8DSAYTkgifql0TliPzqInvi0k10nboeHz9Zy24FcDP21iPEyCQmp+C1b3fg7NVbKFMoGJO61TG2W53/t06DliVCckmNEmFY+lpTNK1YGLcSk9VFd+yy/eoJ1RS99dJXQo+L1ubLckIMjoTrjvhxL9YduYggPx9M71kfYUEGrqPG/1unQrFESB4oGOKPuc83xMvNK6jpL9Ydx1OfbzRuPaZc9tYjxGhM+/0o5m89DTEkffpMXVQtbuDkHP7fOh2KJUIcEMc0rH0VFfwdGuiLXaev4tEpf2L5nmh4Um89QozC0l3nUtsZSeHJ1tWyWflfr/D/1ulQLBHiINpWj8DyN5qhXulwXL+dhAHztuNfi/fgdmKyaXvrSQ+tjUcv4aedZ9VYpgnRM38evoAhC3ap188/WA59HtSyWz25JybJGgZ4E+JAShYIxncvNcGklX9j2tqjmLf5FLadvIJPutdVAeFm6q0XtTcao5fuR/S126mzpZifNCKW5ruE6I2tJy7jxS+34U5yCtrXiMC/OlSFKXBAT0xiH1qWCHEwfj7eeKddFRXLVCjEHwdjrqPTp+sw449j+re8ZLO3XlR8Obzy9XYboSTEXLut5ouQcja0apGcsOfMNTw/e6tKxmh+XxH1ACMudFPgwJ6YJGNYwdsBsIK3B5DLauCx12/jnUW7sebQBTXdsFxBVaOpVMFg6D6rRpH28qBdiJOfmoumS/LdI5TSrhURFoh177R02s3ITFYtEXlbjl9Wv5Wi+QPVb8Q0N3GdcCA6Dj1mbMKVm4loVK4g5vRtiCD/vBWpNdr/bW5aPXkCcdm8f1MsOQCKJfe2IdF77RL5F5Osm/eX7cfNO8kI8ffBqE7VVZNO3faeyvCYS6gmxBsDHsQzMzZluYlv+zdGkwqFnCKUxHqV/sJl/San9axnGMFkJtGn1//53Weu4rmZW1Sl/dqlwlUrk3wBJo1AsfN/S6GUMRRLLoRiycRF01Kf1jK5Nefgae3UpZsYsnAntp64oqalWvDYx2qgeFgQjHSDE7eX9MjLik+610GXOiUcboVpOn61W61ajsJMok+v//N/nbiMvrO34npCEuqWDlcWJUPXUjLjw6ibYW84oj+MVjTNwbVLShcKxvwXm2B4+yrw99FapTwy6Q98tekkUvQYb5NJbz1xFWWH7K6XE8RdlZlQEuRblOWynp4R0ScWJTu/LLXc8HFYbvyf33DkorIoiVAS19tX/RqZXyg5oScm0aBYIq7BiEXTnFC7RKwdLzWvgJ9fb6pKDMQnJKkqwk9/vhFHYuNhBCSmRlxFdkJJ1XJZz9FIXI8j13MXZhF9ev2fF+tnn7vB3M0qFVYWJdO63ohLoFgirsGIRdOcWLukUrH8WPjyAxjdubqKYfrr5BU8+smfmLLqsO7bpYjgk5gaIb1gsk7Lcme4wdxp1XIkZhF9evufl6gSKdkhbmJreYAvejcwXzA3cTkUS8Q1GLFompNrl4iY6P1AWfw6uDlaVC6iLu5Sn6n9J39g/ZGL0HM383YhRzDt2doqPigtMu3MWBt3WrUciVlEn57+55OSUzDip70YH3VQTfdrWg5Te9RDgC+FEsk7tEsS12DEomnW2iUSX5GhK0Fql0TmuXZJifAgzO5zP5bsOocxS/fj6IUbePaLzehQs7gqmhcZHqTLIN12oZF4pPNH2BLY1GVp71arlgRGe2WcIO00q5YzRJ/Upcrkl6WEp95Fn17+56/evIPX5+/EH39fgCSYjuhQDc83NUFlbqIbaFkirsGIRdMkMFIyduw5nCQl1wEBlFJCQDLHVg9pgd5Nyqjmnj/viUar//yOz9YeQUJSsi6DdH0W9kaThPVq36VMgCtEilitxHrlaquWWVyZZvufPxgTh87/W6+EUqCfN6Y9W49CiTgclg5wACwdYPKiaW6oXbL/XBxGLdmbWmagfOEQ/LtjVTxcuahrazOJ621yDTuxJ3eta4P2uDzrxgzFHE1fZ8nJ//PSrPqthbtU/bKSBYLwf881QLVIXoNJ9mGdJRdCseQBRdPcULtE/jUX7ziLD5cfxMX4BDXvgQqF8O6jVVGjRBhcgsQoze2Y9Xq9l2lpysQjRZ+r/+clCeLjqIP4Yt1xNf1gxUL43zP1UCDE31F7TTyEOIol10GxlENYNC1HxN1OxNTVRzB7wwl1kxDD0mN1SuCttpWdH8+0ZxHwfb+s13tiplbXhRAn/8+fuHgDr8/fgd1nrqnp/s3KqV6Mvj6MKiE5h2LJhVAsEVdw+vJNTPz1EH7aqT2hB/h6q9iMV1pUQGigk4rt0bJEdMSPO87iX4v34MadZIQH++HjJ2qhTfUId+8WMTAUSy6EYom4Eul1NfbnA6kFC6Uq8YsPlUefB8oixNGF91JjlrLICHRDzBLxHCTb7b0l+/Dj3QcFcVVKOx3dtgoihoFiyYVQLBFXI/+2vx2IVTVlrJW/C4X44+XmFfBckzII9HOgcDFqYD4xBb/ui8G7i/equD0J5Xq9VSW81rKSueK6iNugWHIhFEvEncHBS3adxSe/HcaJSzfVvCL5AzDw4Yro3rCU4wryGTUwnxiWKzfu4L2l+1LdzhWL5sPEp2qjTqlwd+8aMREUSy6EYom4G6le/MP2s/hk1WGcvXpLzSsWGoD+zcrjmYalHeOeY2A+cQFyS/p++1mMW34Al27cUdYk6af4RqtKjrWYEoLs378Nkz5w+fJlPPvss+pgwsPD0a9fP8TH22882qJFC1WTJu3w8ssv26xz6tQpdOjQAcHBwShatCjefvttJCUlOfloCHEskgn09P2lsOatFni/aw1EhAbifFyCim16cPxq/Hfl3+pJPU+wmzlxMlJgUppKS+0kEUqViubDDwMeVNluFErEnRjGstS+fXtER0fj888/R2JiIvr27Yv7778f33zzjV2xdN9992HMmDGp80QUWdVjcnIy6tSpg4iICEyYMEFtv1evXujfvz8+/PDDbO8bLUtEb0jF78Xbz2L670dT3XPB/j7KyiTWpvTVr4mH10LSQXmMT1cdxqz1J9R3HeTngzdaV8LzD5aDv69hnumJATGVG+7AgQOoVq0atm7digYNGqh5UVFRePTRR3HmzBlERkpJ/YzFkoihyZMnZ7h8xYoV6NixI86dO4dixbT+RNOnT8c777yDCxcuwN8/ewXOKJaIXpEbj1Q5/mztURyIjlPz/Hy8VN+5vg+WQ23Gf2SJ6atsu5HE5BR8s/mUch9fvmv5bFc9AiM6VVM9EwUKVTvQNZ5nTCWWZs2ahSFDhuDKFa31gyCussDAQCxcuBCPPfZYpmJp3759ygcu1qNOnTphxIgRyrokjBw5EkuWLMHOnTtT33P8+HGUL18e27dvR926dTPcbkJCghrSftmlSpWiWCK6Rf4H1v59AdPWHMWWE1rJAaF+mQLo+2BZdYNiUb+MhZI07U1/kbTeqo3Si06Pv8eovTH4+JdDOH7xhppXvkgIRnSsplr6WKFQzWnSRaTWz5JJFw4XSw4uyuIcYmJiVDxRWnx9fVGwYEG1LDN69OiBMmXKKMvT7t27lcXo0KFD+OGHH1K3a7UoWbFO29vuuHHjMHr06DweFSGuQ+L15CYkw54z1zB7/XEs3X0O205eUYPcgHo1KYtu95dCQbaMSLVoyI06o6dJy13BJMsfqRZBS0cORNK6IxdVDN32U1fVvML5/PFG6/vQ/f5S8Esj2DMTqjHXbqv5Hi1UU8t5pPt2pB6azGc5D4fjVrE0bNgwjB9v7eqeuQsut7z44oupr2vWrInixYujVatWOHr0KCpUqJDr7Q4fPhyDBw++x7JEiBGoWTIMk7rVwbD2VfD15lP4ZvNJ9eQuNZvkJta2RgSeaVgKTcoXcm3TXp0hrp+0Fo30yG1Klst6TSoUcum+GTFbc+a64/h2y6nUGDqJS5JWJS82r4B86bI19ShUdeMOFNebWJTsfTtRw4AqHeiSM4tYEtdanz597K4jLjFxocXGxtrMFzecZMjJsuzSqFEjNT5y5IgSS/LeLVu22Kxz/vx5Nba33YCAADUQYmSKhgZi8CP3YUCLCli2OxpzN5zAnrPXsHTXOTWULRSM7g1L48n6JVE4n+f93uWm6Mj1PNWSJC16/u+PY0hM/ufmLskGozpWQ7eGpQ0hVHXlDpQYpbSut3uwAHFntfXYgsgcYqlIkSJqyIomTZrg6tWr2LZtG+rXr6/mrV69GikpKakCKDtYY5PEwmTd7gcffKCEmNXNt3LlSuW3lIByQjwBSckWQSTD3rPX1NO/FAIUC8BHKw5i4i+H0LpqMTxWr4Ry43lKdpJYDxy5nidxOzFZ9XGTwO2MRM+tO8kY9sMehAX7ZSg29CRUdecOlGBuR65HsoUhrnpVq1ZFu3btVEq/WILWr1+PgQMHonv37qmZcGfPnkWVKlVSLUXianv//feVwDpx4oQK5JayAA899BBq1aql1mnTpo0SRc899xx27dqFX375Bf/+97/x6quv0nJEPJIaJcLwwWM1sfndVqpJqVRLTkqxIGpfDF76ahsafvibamS67eRlZTUwM+JmEetBZo4WmS/LZT2icSk+QVWTbzp+tRJDmVmHrL8csdaIe0uvQjUrd6C9Y3AakvXmyPVItjBEgLcwb948JZAk5sjb2xtPPPEEpkyZkrpcai9J8PbNm5o/XNL+f/vtN1U24MaNGyqmSN4jYsiKj48Pli1bhldeeUVZmUJCQtC7d2+bukyEeCJS8VuKXMogJQd+2H5GWZtirydg3uZTaihdMBhd60SiU+1IVCqWH2ZD4lHEzSLWA6+Mu+Kp5Z4e3C2iefPxy6oEgGS43UlOSe1VKIUlc+NKswpVsd5k0r5Z1QpztlB1qzsws7IAMpast6yaW8t6xGEYonSA3mGdJeIJyNPzhqMXsXjHWXVTvHknOXVZhSIheLRmcbSrEYFqxUNNFRiuq3gVF5GdYGapi/T9tjPKbXvsbvq/ULtkGPo1K6+Cugcv2JXlZ33SvQ661CmRqfsLmQhVV7i/ftp5Fm/M/6e0TE6PwWllAdjc2mGYqs6S3qFYIp7GzTtJWLn/vLI2/Xn4gk3wbplCwUo0ta9RHLVKhMHbBJYX3WRCuVkctqhcFKsOxOLHnWex9lBs6nkP8fdB5zol0KNhaZVtKWw8egnPzNiU5ed9279xplYZdwtVRxyDw8oCpBdCbG7tECiWXAjFEvH0VhWrD8Rixd5orD10AQlJmhvGWkOn+X1F8XCVImhWqQjCgvzcuq8kd8HMVgL9vHE78Z/zW7NEGHo0Kq1csRml/0vsUlautHXvtLQrPN0pVB11DDlyvU2uYSfb7a6LbdAezSXHCt55hmLJhVAsEaJxIyFJCSarcIpP+KcptdxMpGK4ZNQ1v68IqkTkhzdSjHmxN/pNKoP9T4a3Egb2YnSEyPBA5XLqWqcEKkfYj1XTgystr7j0GI7/CcztmPV6vZexLICDoFhyIRRLhNzLnaQU/HXyshJNqw/G4khsvM3yAgEWNMYePJD8F5p470cFr3PwCjNAuwajt5nIZP/XVx2BZ3/XXGj2mPdCIzxYsXC2P87drjRH4LJj2LMI+L5f1us9MROo+aTjPteDiaNYch0US4RkzenLN7HmUCzWHIzF5qOxuJlk67Yogit4wHs/GngfQr12vVG5SUf99avLbjyJXkmz/zcsAdiUUg1/ptTEupSaOGLJXoByboKZzRDz5ZJjoGXJ5VAsuRCKJUJyQEoyEv9bC7uvBWFDSnVsTKmGv1Luwx3Y9qSTKs+1S4ajXplw1CtdAHVLF3Bv37qcxpPojEtxt7Bt6nPYFl9Yfd+7LBWQlKZ6jLhEq3idwn5LmTROJicHM5vVzZnn31gWZQF0+hszIqZqpEsIMREnN8Dv+hnU9wbqex/Ga/gRty1+2JFSUQmnHZZK6nX8nWBsPHZJDVZKhAehWmSoKk8g4+qRoWqeS0oV5LHNhCutK1Ih+2BMHPaei8Pu01dVs2Qtvb+vzXqlvGLRzHs3mnnvUVa9MK8b6H7n38ri5K7aRoZ3c+YFEUBynMr6l0l1L8l2o1ByORRLhBDXkkEbhkCvRDTxOaAGIdnihSOtZ2J7wP3YfvIKtp+6gqMXbuDs1VtqkLIFVkIDfVGleCgqFs2HCkVkCFFjEVEOLVuQhzYTzop5SUmxqO9DhNDfMdex79w17DsXh6MX4pFRUelKXmfQwPtv1PP6Gw29D6KMt23PTaEorrqvCGdmbk6xtMh8vbs5HYEcnxxnhoKRZQHcBcUSIcS1ZKMNg4+XBZVLFkXlcqXxzN1mq1Ki4MC5OOyPjlOCYP+5OByOvY6420nKYiNDWgJ8vVGucAjKFgpBiQJBSjyVlHGBIJQMD0ZokG/OLFK5bDOR195iCUnJal0RReeu3sapSzeUcBRBdPziDZtSDWmR5sdieatRIlRlIdazHEL4d0Oz3P2ere/H1s2BNsIuwhUB2eKCEoGQaXMRLyBqGFClg/ktKyKI5Dg90RWpUyiWCCGuJZftGkID/dCofCE1pM24E8F0KOY6jt0VEDK2ioiDMdfVkBFSF6ho/gAlKgrn91fjQiHaaxmLxSqfDAG+yB/oh/yRjRCQPxJe17O/39npLfbuD3uU2+zKzURVFVtahFy+kYCYuAScu3oLF64n2P06/X28UbZwsLKmiXuyeolQ1IgMQ9HQdH3TUgpn63tv2KIT1rXwdn1Adh7dnKZDhJEnHKdBoFgihBg2LsPf1xvVI8PUkBYRKWeu3FTi6dSlm6nuuzNXbuHslVtKkEgNKBnSturICl+vCQiy3IKfCo1OVmM/r+TUacud0kievA7JFotykd1ISMaFePti5/LNRLyZRVsQKQYZGf6Pdax84XyoUPQfd2O2sgZz8L3LN+/0IG4HujkJcTYUS4QQ08VliBWkTKEQNWTWrkVcWmK1uRifgEvxMr6jXstYLDsipK7fTkK8DHeSIHnDSRYvXEew7cbSao6r8se2nlR2kHgrKfAozWcl40/GRfIHoER4sHIbFgj2c0wQu57jYXLp5iTEFbB0gANg6QCiC4yYbm2QfRYr0c3EZCWcRGglJSUh8cxOJMZfQlJgQdwpXF0JKW8vL3h7S8yVlxJsB6LjMOKnfVlu3yXp+Hr/3pk2T9wASwcQ4kkYNd3aIHEZklUnsUs2/c+Kt8jyfVIb6rO1R7PsLeb0dHwjfO9Mmyc6RmflcQkhuU63Th8ca023luXELYh1SbLIhPRONJel4xsJq5swNF3WnQh/TygbQHQL3XAOgG444jYMXlXaUzBDfzSXokc3ITEldMMR4gkw3doQiCB6pFqE4fujebSbkHg0FEuEGBmmWxsGEUYuT8cnhDgExiwRYmSYbk0IIU6HYokQM1TDzrRLvMQslbinGjYhhJDsQ7FEiJGxplvby7diujUhhOQJiiVCjA7TrQkhxKkwwJsQM8Au5YQQ4jQolggxC0y3JoQQp0A3HCGEEEKIHSiWCCGEEELsQLFECCGEEGIHiiVCCCGEEDtQLBFCCCGE2IFiiRBCCCHEDhRLhBBCCCF2YJ0lQggh5iQlmYVaiUOgWCKEEGI+9i8Bot4B4s7ZtgCSXopsAURyCN1whBDPRKwOx/8E9izSxjJNzCOUFvSyFUpCXLQ2X5YTYkaxdPnyZTz77LMIDQ1FeHg4+vXrh/j4+EzXP3HiBLy8vDIcFi5cmLpeRsvnz5/voqMihLgFuVlOrgHM7Qh8308byzRvosZHRK9YlGDJYOHdeVHDKI6JOcWSCKV9+/Zh5cqVWLZsGf744w+8+OKLma5fqlQpREdH2wyjR49Gvnz50L59e5t1Z8+ebbNe165dXXBEhBC3QKuDuZEYpfTn1gYLEHdWW48QM8UsHThwAFFRUdi6dSsaNGig5n366ad49NFHMXHiRERGRt7zHh8fH0RERNjMW7x4MZ5++mklmNIilqr069ojISFBDVbi4uJycVSEEP1ZHbw0q0OVDvoPBGbwcsbI9+HI9QgximVp48aNStBYhZLQunVreHt7Y/PmzdnaxrZt27Bz507lvkvPq6++isKFC6Nhw4aYNWsWLJaMLqT/MG7cOISFhaUOYsUihBgAs1gd6EbMHBGOjlyPEKOIpZiYGBQtWtRmnq+vLwoWLKiWZYeZM2eiatWqeOCBB2zmjxkzBgsWLFDuvSeeeAIDBgxQVit7DB8+HNeuXUsdTp8+nYujIoS4HDNYHehGtI9Y2CTrTayEGeIFhJbQ1iPECGJp2LBhmQZhW4eDBw/m+XNu3bqFb775JkOr0ogRI/Dggw+ibt26eOeddzB06FBMmDDB7vYCAgJUoHnagRBiAIxudWDwctaIK1LKAyjSC6a70+0+osuSGCdmaciQIejTp4/ddcqXL6/iiWJjY23mJyUlqQy57MQaLVq0CDdv3kSvXr2yXLdRo0Z4//33VUySiCJCiAmtDmKFyVBwiNUhUr9Wh5y4Ecs1g8cidZSe/jKTOksfsc4SMZZYKlKkiBqyokmTJrh69aqKO6pfv76at3r1aqSkpChxkx0XXOfOnbP1WRLXVKBAAQolQsxsdRB3lbIyWIxldTCDG9FViCCSQH0GwRNPyYaTWKN27dqhf//+mD59OhITEzFw4EB07949NRPu7NmzaNWqFb788ksVqG3lyJEjqszA8uXL79nu0qVLcf78eTRu3BiBgYEqbunDDz/EW2+95dLjI4S4ECNbHYzuRnQ1Iow82cJGPEssCfPmzVMCSQSRZMFJMPaUKVNSl4uAOnTokHK3pUWy20qWLIk2bdrcs00/Pz9MnToVb775psqAq1ixIiZNmqREGSHExBjV6mB0NyIhBsXLklWePMkSqbMkJQQkM47B3oQQl2TDKTJwI4rVTM/WMUIMeP82ROkAQggh6dyIocVt54tFiUKJEM92wxFCCDG4G5EQg0KxRAghRoTBy4S4DLrhCCGEEELsQLFECCGEEGIHiiVCCCGEEDtQLBFCCCGE2IFiiRBCCCHEDhRLhBBCCCF2oFgihBBCCLEDxRIhhBBCiB0olgghhBBC7MAK3g7A2otYGvIRQgghxBhY79vW+3hmUCw5gOvXr6txqVKl3L0rhBBCCMnFfTwsLCzT5V6WrOQUyZKUlBScO3cO+fPnh5eXF/SqnkXMnT59GqGhoTAznnSsnna8PFbz4knHy2PVDyKBRChFRkbC2zvzyCRalhyAfMElS5aEEZAfqx5/sM7Ak47V046Xx2pePOl4eaz6wJ5FyQoDvAkhhBBC7ECxRAghhBBiB4olDyEgIACjRo1SY7PjScfqacfLYzUvnnS8PFbjwQBvQgghhBA70LJECCGEEGIHiiVCCCGEEDtQLBFCCCGE2IFiiRBCCCHEDhRLJuHy5ct49tlnVdGv8PBw9OvXD/Hx8Zmuf+LECVVtPKNh4cKFqetltHz+/Pkw2vEKLVq0uOdYXn75ZZt1Tp06hQ4dOiA4OBhFixbF22+/jaSkJBjpWGX91157DZUrV0ZQUBBKly6N119/HdeuXbNZTy/ndurUqShbtiwCAwPRqFEjbNmyxe768vusUqWKWr9mzZpYvny5zXLJWRk5ciSKFy+ujr9169Y4fPgw9EBOjnXGjBlo1qwZChQooAY5jvTr9+nT555z2K5dOxjtWOfMmXPPccj7zHheM7oOySDXHb2f1z/++AOdOnVS1a5ln3788ccs37N27VrUq1dPZcNVrFhRneu8XgPcgmTDEePTrl07S+3atS2bNm2y/Pnnn5aKFStannnmmUzXT0pKskRHR9sMo0ePtuTLl89y/fr11PXkJzJ79myb9W7dumUx2vEKzZs3t/Tv39/mWK5du2bzndSoUcPSunVry44dOyzLly+3FC5c2DJ8+HCLkY51z549lscff9yyZMkSy5EjRyyrVq2yVKpUyfLEE0/YrKeHczt//nyLv7+/ZdasWZZ9+/ap8xMeHm45f/58huuvX7/e4uPjY/n4448t+/fvt/z73/+2+Pn5qWO28tFHH1nCwsIsP/74o2XXrl2Wzp07W8qVK+f2321Oj7VHjx6WqVOnqt/igQMHLH369FHHdebMmdR1evfurX4fac/h5cuXLe4mp8cqv8PQ0FCb44iJibFZxyzn9dKlSzbHuXfvXvWblu9A7+d1+fLlln/961+WH374QV0/Fi9ebHf9Y8eOWYKDgy2DBw9W/6+ffvqpOtaoqKhcf3/ugmLJBMiPUH64W7duTZ23YsUKi5eXl+Xs2bPZ3k6dOnUszz//vM287PxDGOV4RSy98cYbdi8E3t7eNhfpadOmqYt4QkKCxcjndsGCBeqClJiYqKtz27BhQ8urr76aOp2cnGyJjIy0jBs3LsP1n376aUuHDh1s5jVq1Mjy0ksvqdcpKSmWiIgIy4QJE1KXX7161RIQEGD59ttvLUY61vSImM+fP79l7ty5NjfVLl26WPRGTo9VhIIIocww83n973//q85rfHy87s9rWrJz/Rg6dKilevXqNvO6detmadu2rcO+P1dBN5wJ2Lhxo3LPNGjQIHWemKilZ93mzZuztY1t27Zh586dysWTnldffRWFCxdGw4YNMWvWLGUON+rxzps3Tx1LjRo1MHz4cNy8edNmu+LWKVasWOq8tm3bqkaQ+/btg1HPrSAuOHHj+fr66ubc3rlzR/3u5HisyHHJtBx3Rsj8tOtbz5F1/ePHjyMmJsZmHen7JKb9zLap12NNj/xWExMTUbBgwXvcHOIyFrfrK6+8gkuXLsGd5PZYxbVcpkwZ1XS1S5cuNv9zZj6vM2fORPfu3RESEqLr85obsvp/dcT35yrYSNcEyEVE/qnSIjdFuajKsuz+w1atWhUPPPCAzfwxY8agZcuWKobn119/xYABA9RFTWJgjHa8PXr0UBdj8bfv3r0b77zzDg4dOoQffvghdbtphZJgnc7u96jHc3vx4kW8//77ePHFF3V1bmW/kpOTM/zODx48mOF7MjtH1u/COra3jjvIzbGmR36v8ttNe2OROJbHH38c5cqVw9GjR/Huu++iffv26kbj4+MDoxyrCAIR67Vq1VLCfuLEiepaJIJJmpSb9bxKbM7evXvV9TctejyvuSGz/1d5AL116xauXLmS5/8LV0GxpGOGDRuG8ePH213nwIEDef4c+dF+8803GDFixD3L0s6rW7cubty4gQkTJjjlhurs400rFsSCJIGirVq1UhejChUqwIznVi5KEjharVo1vPfee247tyRvfPTRRyr4XqwNaQOfxSKR9jctYkN+y7Ke/LaNQpMmTdRgRYSSPLx9/vnnSuibFRFJct7EspsWs5xXM0GxpGOGDBmisiLsUb58eURERCA2NtZmvmRwSVaULMuKRYsWKRN/r169slxXzN5y8UpISHB4rx9XHW/aYxGOHDmiLkTy3vRZGOfPn1fjnGxXL8d6/fp19YSaP39+LF68GH5+fm47txkh7j95SrZ+x1ZkOrNjk/n21reOZZ6I4bTr1KlTB+4iN8dqRawsIpZ+++03ddPM6jcjnyW/aXfdVPNyrFbktyoCXo7DrOdVHk5EAIuFNyv0cF5zQ2b/rxISIBmN8t3l9bfiKhizpGOKFCmiUqTtDf7+/uqJ7OrVq8r3a2X16tVISUlJFQRZPd107txZfV5WSFyTpDE742bqquNNeyyC9eIr292zZ4+NOFm5cqX6xxbLjJGOVSxKbdq0UdtYsmTJPWnYrj63GSH7Vr9+faxatSp1nhyXTKe1MqRF5qdd33qOrOuL20IusmnXke9C4rsy26Zej1X4+OOPlYCNioqyiVvLjDNnzqjYlrSCwijHmhZxzcj/ovU4zHZerSUw5MGkZ8+ehjivuSGr/1dH/FZchrsjzIljkDTTunXrWjZv3mxZt26dShVPm14u6caVK1dWy9Ny+PBhlVklGVbpkdTzGTNmqLRsWe+zzz5TaaAjR460GO14JYV+zJgxlr/++sty/Phxy08//WQpX7685aGHHrqndECbNm0sO3fuVOmtRYoU0UXpgJwcq5RDkAyxmjVrquNOm34sx6incytpw5LRNGfOHJX59+KLL6q0YWtG4nPPPWcZNmyYTekAX19fy8SJE1U6/ahRozIsHSDbkHO8e/dulVWklxTznByrHIdkMC5atMjmHFpLe8j4rbfesmzcuFH9pn/77TdLvXr11O/j9u3bFiMdq5Qt+eWXXyxHjx61bNu2zdK9e3dLYGCgSiU323m10rRpU5UZlh49n9fr16+rUhYyiHyYNGmSen3y5Em1XI5Tjjd96YC3335b/b9KKYyMSgfY+/70AsWSSZDaHXIDlTpJkuret29fm3pJ8k8nP+41a9bYvE+EQKlSpVS6ZnpEQEk5AdlmSEiIqvUzffr0DNfV+/GeOnVKCaOCBQuqf0ypVST/wGnrLAknTpywtG/f3hIUFKRqLA0ZMsQm3d4Ixypjmc5okHX1dm6l9krp0qWVMJA0Yqknlbbcg6RRpy+DcN9996n1JS35559/vifNfMSIEZZixYqpc92qVSvLoUOHLHogJ8dapkyZDM+hCETh5s2bStiLoBfBKOtLjRq93GRycqyDBg1KXVfO26OPPmrZvn27Kc+rcPDgQXUuf/3113u2pefzuiaTa4v1+GQsx5v+PXKtke9GHlDT1pPKzvenF7zkj7utW4QQQggheoUxS4QQQgghdqBYIoQQQgixA8USIYQQQogdKJYIIYQQQuxAsUQIIYQQYgeKJUIIIYQQO1AsEUIIIYTYgWKJEEIIIcQOFEuEEEIIIXagWCKEEDdw+/Zt9OnTBzVr1oSvry+6du3q7l0ihGQCxRIhxGORbk9JSUlu+ezk5GQEBQXh9ddfR+vWrd2yD4SQ7EGxRAgxDC1atMDAgQPVEBYWhsKFC2PEiBFK9AhfffUVGjRogPz58yMiIgI9evRAbGxs6vvXrl0LLy8vrFixAvXr10dAQADWrVuHo0ePokuXLihWrBjy5cuH+++/H7/99pvNZ5ctWxZjx45Fr1691DplypTBkiVLcOHCBfVemVerVi389ddf2TqWkJAQTJs2Df3791f7SgjRLxRLhBBDMXfuXOW22rJlCz755BNMmjQJX3zxhVqWmJiI999/H7t27cKPP/6IEydOKFdXeoYNG4aPPvoIBw4cUAInPj4ejz76KFatWoUdO3agXbt26NSpE06dOmXzvv/+97948MEH1TodOnTAc889p8RTz549sX37dlSoUEFNsz85IebCy8L/akKIgSxLYinat2+fshBZhY9YePbv33/P+mLlESvR9evXleVHLEsPP/ywElJiDbJHjRo18PLLLysrltWy1KxZM2W9EmJiYlC8eHFl2RozZoyat2nTJjRp0gTR0dE5shaJoLt69araL0KI/qBliRBiKBo3bpwqlAQRJ4cPH1YxQNu2bVMWodKlSytXXPPmzdU66S1E4qpLi1iW3nrrLVStWhXh4eFKWInVKf37xAplRVx2ggRop5+X1vVHCDE+FEuEENNkl7Vt2xahoaGYN28etm7disWLF6tld+7cuSdeKC0ilGTdDz/8EH/++Sd27typRFD69/n5+aW+tgq2jOalpKQ44QgJIe7C122fTAghuWDz5s020+L6qlSpEg4ePIhLly6pWKRSpUqpZdkNtl6/fr1yhT322GOpliaJdyKEEIGWJUKIoRDX2ODBg3Ho0CF8++23+PTTT/HGG28o15u/v7+aPnbsmIpjkmDv7CBi64cfflAWJQkOlyw6V1iHJM5KPvPy5cu4du2aei0DIURf0LJECDEUkm1269YtNGzYED4+Pkoovfjii8oFNmfOHLz77ruYMmUK6tWrh4kTJ6Jz585ZblMy6p5//nk88MADqhzBO++8g7i4OKcfi2TgnTx5MnW6bt26asy8G0L0BbPhCCGGyoarU6cOJk+e7O5dIYR4EHTDEUIIIYTYgWKJEEKcQPv27VUJgowGybojhBgHuuEIIcQJnD17VsVWZUTBggXVQAgxBhRLhBBCCCF2oBuOEEIIIcQOFEuEEEIIIXagWCKEEEIIsQPFEiGEEEKIHSiWCCGEEELsQLFECCGEEGIHiiVCCCGEEGTO/wOFG26zHpzIUAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig5 = plt.figure()\n",
    "passed = plt.scatter(data.loc[:, \"param_1\"][mask], data.loc[:, \"param_2\"][mask])\n",
    "failed = plt.scatter(data.loc[:, \"param_1\"][~mask], data.loc[:, \"param_2\"][~mask])\n",
    "plt.plot(X1_range, X2_new_boundary1)\n",
    "plt.plot(X1_range, X2_new_boundary2)\n",
    "plt.title(\"param_1 vs param_2\")\n",
    "plt.xlabel(\"param_1\")\n",
    "plt.ylabel(\"param_2\")\n",
    "plt.legend((passed, failed), (\"Passed\", \"Failed\"))\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
